diff --git a/code/IndShock.ipynb b/code/IndShock.ipynb
index d02e09e..cc09b03 100644
--- a/code/IndShock.ipynb
+++ b/code/IndShock.ipynb
@@ -1,675 +1,675 @@
 {
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "***NOTE: using a 'quick fix' for an attribute error. See 'Error Notes' in EstimationParameter.py for further discussion.***\n"
-     ]
-    }
-   ],
-   "source": [
-    "from estimark.agents import IndShkLifeCycleConsumerType\n",
-    "import estimark.calibration.estimation_parameters as parameters\n",
-    "import numpy as np\n",
-    "from HARK.utilities import plot_funcs\n",
-    "import matplotlib.pyplot as plt\n",
-    "import pandas as pd\n",
-    "from HARK.ConsumptionSaving.ConsIndShockModel import init_lifecycle"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "DiscFacAdj, CRRA = np.genfromtxt(\n",
-    "    \"tables/IndShock_estimate_results.csv\", skip_header=1, delimiter=\",\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1.3759978446748666, 0.9552205116274122)"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "indshk_agent = IndShkLifeCycleConsumerType(\n",
-    "    **{**init_lifecycle, **parameters.init_consumer_objects}\n",
-    ")\n",
-    "indshk_agent.CRRA = CRRA\n",
-    "indshk_agent.DiscFac = [b * DiscFacAdj for b in parameters.DiscFac_timevary]\n",
-    "CRRA, DiscFacAdj"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "indshk_agent.solve()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+    "cells": [
+        {
+            "cell_type": "code",
+            "execution_count": 1,
+            "metadata": {},
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "***NOTE: using a 'quick fix' for an attribute error. See 'Error Notes' in EstimationParameter.py for further discussion.***\n"
+                    ]
+                }
+            ],
+            "source": [
+                "from estimark.agents import IndShkLifeCycleConsumerType\n",
+                "import estimark.calibration.parameters as parameters\n",
+                "import numpy as np\n",
+                "from HARK.utilities import plot_funcs\n",
+                "import matplotlib.pyplot as plt\n",
+                "import pandas as pd\n",
+                "from HARK.ConsumptionSaving.ConsIndShockModel import init_lifecycle"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 2,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "DiscFacAdj, CRRA = np.genfromtxt(\n",
+                "    \"tables/IndShock_estimate_results.csv\", skip_header=1, delimiter=\",\"\n",
+                ")"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 3,
+            "metadata": {},
+            "outputs": [
+                {
+                    "data": {
+                        "text/plain": [
+                            "(1.3759978446748666, 0.9552205116274122)"
+                        ]
+                    },
+                    "execution_count": 3,
+                    "metadata": {},
+                    "output_type": "execute_result"
+                }
+            ],
+            "source": [
+                "indshk_agent = IndShkLifeCycleConsumerType(\n",
+                "    **{**init_lifecycle, **parameters.init_consumer_objects}\n",
+                ")\n",
+                "indshk_agent.CRRA = CRRA\n",
+                "indshk_agent.DiscFac = [b * DiscFacAdj for b in parameters.timevary_DiscFac]\n",
+                "CRRA, DiscFacAdj"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 4,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "indshk_agent.solve()"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 5,
+            "metadata": {},
+            "outputs": [
+                {
+                    "data": {
+                        "image/png": 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",
+                        "text/plain": [
+                            "<Figure size 640x480 with 1 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "data": {
+                        "text/plain": [
+                            "<Figure size 640x480 with 0 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                }
+            ],
+            "source": [
+                "plot_funcs([sol.cFunc for sol in indshk_agent.solution[:-1:5]], 0, 20)\n",
+                "plt.savefig(\"../content/figures/IndShock_cFunc.png\")"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 6,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "# Set up the variables we want to keep track of.\n",
+                "indshk_agent.track_vars = [\"aNrm\", \"cNrm\", \"pLvl\", \"t_age\", \"mNrm\"]\n",
+                "\n",
+                "indshk_agent.T_sim = 200\n",
+                "# Run the simulations\n",
+                "indshk_agent.initialize_sim()\n",
+                "history = indshk_agent.simulate()"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 7,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "raw_data = {\n",
+                "    \"Age\": indshk_agent.history[\"t_age\"].flatten() + 25 - 1,\n",
+                "    \"pIncome\": indshk_agent.history[\"pLvl\"].flatten(),\n",
+                "    \"nrmM\": indshk_agent.history[\"mNrm\"].flatten(),\n",
+                "    \"nrmC\": indshk_agent.history[\"cNrm\"].flatten(),\n",
+                "}\n",
+                "\n",
+                "Data = pd.DataFrame(raw_data)\n",
+                "Data[\"Cons\"] = Data.nrmC * Data.pIncome\n",
+                "Data[\"M\"] = Data.nrmM * Data.pIncome"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 8,
+            "metadata": {},
+            "outputs": [
+                {
+                    "data": {
+                        "image/png": 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",
+                        "text/plain": [
+                            "<Figure size 640x480 with 1 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                }
+            ],
+            "source": [
+                "# Find the mean of each variable at every age\n",
+                "AgeMeans = Data.groupby([\"Age\"]).median().reset_index()\n",
+                "\n",
+                "plt.figure()\n",
+                "plt.plot(AgeMeans.Age, AgeMeans.pIncome, label=\"Permanent Income\")\n",
+                "plt.plot(AgeMeans.Age, AgeMeans.M, label=\"Market resources\")\n",
+                "plt.plot(AgeMeans.Age, AgeMeans.Cons, label=\"Consumption\")\n",
+                "plt.legend()\n",
+                "plt.xlabel(\"Age\")\n",
+                "plt.ylabel(\"Thousands of USD\")\n",
+                "plt.title(\"Variable Medians Conditional on Survival\")\n",
+                "plt.grid()"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 9,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "indshk_agent = IndShkLifeCycleConsumerType(**parameters.init_consumer_objects)\n",
+                "indshk_agent.CRRA = CRRA\n",
+                "indshk_agent.DiscFac = [b * DiscFacAdj for b in parameters.timevary_DiscFac]\n",
+                "\n",
+                "lifecycle_agent = IndShkLifeCycleConsumerType(\n",
+                "    **{**init_lifecycle, \"PermGroFacAgg\": 1.0}\n",
+                ")\n",
+                "\n",
+                "\n",
+                "lifecycle_agent.DiscFac = [\n",
+                "    init_lifecycle[\"DiscFac\"] for b in parameters.timevary_DiscFac\n",
+                "]"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 10,
+            "metadata": {},
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "PerfMITShk\n",
+                        "current:  False\n",
+                        "original: False\n",
+                        "\n",
+                        "\n",
+                        "bilt\n",
+                        "current:  {}\n",
+                        "original: {}\n",
+                        "\n",
+                        "\n",
+                        "quiet\n",
+                        "current:  False\n",
+                        "original: False\n",
+                        "\n",
+                        "\n",
+                        "state_now\n",
+                        "current:  {'pLvl': None, 'PlvlAgg': None, 'bNrm': None, 'mNrm': None, 'aNrm': None, 'aLvl': None}\n",
+                        "original: {'pLvl': None, 'PlvlAgg': None, 'bNrm': None, 'mNrm': None, 'aNrm': None, 'aLvl': None}\n",
+                        "\n",
+                        "\n",
+                        "T_age\n",
+                        "current:  65\n",
+                        "original: 65\n",
+                        "\n",
+                        "\n",
+                        "controls\n",
+                        "current:  {}\n",
+                        "original: {}\n",
+                        "\n",
+                        "\n",
+                        "shocks\n",
+                        "current:  {}\n",
+                        "original: {}\n",
+                        "\n",
+                        "\n",
+                        "track_vars\n",
+                        "current:  []\n",
+                        "original: []\n",
+                        "\n",
+                        "\n",
+                        "parameters\n",
+                        "current:  {'cycles': 1, 'CRRA': 5.0, 'Rfree': 1.03, 'DiscFac': array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
+                        "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
+                        "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
+                        "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]), 'LivPrb': [0.998341, 0.998262, 0.99826, 0.998172, 0.99803, 0.99796, 0.997886, 0.997792, 0.997587, 0.99747, 0.997398, 0.997621, 0.997822, 0.997755, 0.997607, 0.997421, 0.99722, 0.996942, 0.996701, 0.996562, 0.996243, 0.996023, 0.995789, 0.995449, 0.995122, 0.994844, 0.994377, 0.993913, 0.993402, 0.992824, 0.992191, 0.991511, 0.990844, 0.990081, 0.989317, 0.988495, 0.987654, 0.986892, 0.986244, 0.985647, 0.984987, 0.984198, 0.983305, 0.982293, 0.981146, 0.979812, 0.97829, 0.976614, 0.974779, 0.972732, 0.970243, 0.967372, 0.964395, 0.9614, 0.958184, 0.954529, 0.950045, 0.944392, 0.937261, 0.928746, 0.9191320000000001, 0.908706, 0.897671, 0.886107, 0.873964], 'PermGroFac': [1.0434056222652845, 1.0399264609207084, 1.0365832831214161, 1.033374850583594, 1.030299977365458, 1.02735752913683, 1.0245464224818346, 1.0218656242341422, 1.019314150844187, 1.0168910677778014, 1.0145954889457791, 1.0124265761638418, 1.0103835386425875, 1.0084656325069654, 1.0066721603448634, 1.0050024707844432, 1.0034559580998452, 1.0020320618449452, 1.0007302665148479, 0.9995501012348152, 0.9984911394763908, 0.9975529988004792, 0.996735340627129, 0.99603787003188, 0.9954603355684468, 0.9950025291176404, 0.9946642857623696, 0.9944454836886139, 0.9943460441123051, 0.9943659312320589, 0.9945051522076688, 0.9947637571644364, 0.995141839223195, 0.9956395345562754, 0.9962570224691691, 0.9969945255082701, 0.9978523095944938, 0.9988306841831407, 0.9999300024499419, 1.001150661503553, 0.6821, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], 'BoroCnstArt': 0.0, 'MaxKinks': 400, 'AgentCount': 10000, 'aNrmInitMean': -0.928110551297082, 'aNrmInitStd': 1.6577133299830675, 'pLvlInitMean': 3.0632665110178623, 'pLvlInitStd': 0.758541123818503, 'PermGroFacAgg': 1.0, 'T_age': 65, 'T_cycle': 65, 'PerfMITShk': False, 'aXtraMin': 0.001, 'aXtraMax': 100, 'aXtraNestFac': 1, 'aXtraCount': 200, 'aXtraExtra': [None, None], 'PermShkStd': [0.2510928434853925, 0.2510928434853925, 0.2401556615138524, 0.2296573637153073, 0.21962767025150254, 0.21009896926797608, 0.20110619927478804, 0.19268653734486382, 0.18487882622158372, 0.1777226720373576, 0.17125715641406813, 0.16551913914067518, 0.16054118533450362, 0.15634923267877057, 0.15296020779851646, 0.15037988115495657, 0.1486012841616015, 0.14760397284848523, 0.14735430340072547, 0.14780671189307767, 0.14890581414586726, 0.15058901548290318, 0.15278927648771473, 0.15543771871404774, 0.15846584612550016, 0.16180726687440725, 0.16539889573485103, 0.16918168433697292, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 'PermShkCount': 7, 'TranShkStd': [0.3528101356710064, 0.3528101356710064, 0.3553133361361021, 0.3570497985321185, 0.358062647002616, 0.3583899558240917, 0.3580655727131512, 0.35711977391104305, 0.3555797914147738, 0.3534702416818744, 0.3508134774404022, 0.3476298787783864, 0.3439380957540673, 0.33975525189104816, 0.335097115786856, 0.32997824645518803, 0.32441211679253057, 0.318411218603437, 0.31198715185906833, 0.305150700243307, 0.2979118945148286, 0.2902800647440016, 0.28226388203493175, 0.27387138987799703, 0.2651100247519064, 0.25598662494655583, 0.24650742572135614, 0.23667803771374105, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 'TranShkCount': 7, 'UnempPrb': 0.05, 'UnempPrbRet': 0.005, 'IncUnemp': 0.3, 'IncUnempRet': 0.0, 'tax_rate': 0.0, 'T_retire': 39, 'vFuncBool': False, 'CubicBool': False, 'neutral_measure': False, 'NewbornTransShk': False, 'P0': 21.397337769996003}\n",
+                        "original: {'cycles': 1, 'CRRA': 2.0, 'Rfree': 1.03, 'DiscFac': 0.96, 'LivPrb': [0.998566, 0.998583, 0.998599, 0.998609, 0.998611, 0.99861, 0.998601, 0.998569, 0.998508, 0.998419, 0.998312, 0.998192, 0.998056, 0.997906, 0.99774, 0.997556, 0.997348, 0.997115, 0.996852, 0.996562, 0.996249, 0.995916, 0.995561, 0.995186, 0.99479, 0.994349, 0.993881, 0.993428, 0.993005, 0.992583, 0.992124, 0.991583, 0.990942, 0.990175, 0.98929, 0.988296, 0.987216, 0.986059, 0.984831, 0.983509, 0.982022, 0.980368, 0.978602, 0.976732, 0.974708, 0.97243, 0.969863, 0.967036, 0.963933, 0.960506, 0.956589, 0.952211, 0.947534, 0.942585, 0.937209, 0.931163, 0.924276, 0.916534, 0.907855, 0.898197, 0.887532, 0.8758360000000001, 0.863084, 0.849246, 0.834296], 'PermGroFac': [1.0419577244168425, 1.042054094671763, 1.0419741368106594, 1.0417252594892359, 1.0413149489133517, 1.0407507570649852, 1.0400402904123685, 1.0391911991207905, 1.038211166778343, 1.037107900648818, 1.0358891224620352, 1.0345625597498684, 1.033135937734556, 1.0316169717741348, 1.0300133603683226, 1.0283327787265384, 1.026582872898661, 1.0247712544676226, 1.0229054958019392, 1.02099312586525, 1.0190416265789661, 1.0170584297334835, 1.0150509144425075, 1.0130264051347238, 1.0109921700764062, 1.0089554204182813, 1.0069233097596944, 1.004902934223072, 1.0029013330314762, 1.000925489582226, 0.9989823330098357, 0.9970787402313407, 0.9952215384681313, 0.9934175082380967, 0.9916733868131232, 0.9899958721369805, 0.9883916271999132, 0.9868672848666431, 0.9854294531556296, 0.9840847209682294, 0.5719733840877946, 0.9892378656535716, 0.9892378656535721, 0.9892378656535716, 0.9892378656535716, 0.9892378656535716, 0.9892378656535716, 0.9892378656535721, 0.9892378656535716, 0.9892378656535721, 0.9892378656535716, 0.9892378656535716, 0.9892378656535721, 0.9892378656535712, 0.9892378656535721, 0.9892378656535721, 0.9892378656535716, 0.9892378656535712, 0.9892378656535721, 0.9892378656535716, 0.9892378656535721, 0.9892378656535716, 0.9892378656535716, 0.9892378656535716, 0.9892378656535721], 'BoroCnstArt': 0.0, 'MaxKinks': 400, 'AgentCount': 10000, 'aNrmInitMean': -0.928110551297082, 'aNrmInitStd': 1.6577133299830675, 'pLvlInitMean': 3.0632665110178623, 'pLvlInitStd': 0.758541123818503, 'PermGroFacAgg': 1.0, 'T_age': 65, 'T_cycle': 65, 'PerfMITShk': False, 'aXtraMin': 0.001, 'aXtraMax': 20, 'aXtraNestFac': 3, 'aXtraCount': 48, 'aXtraExtra': [None], 'PermShkStd': [0.2510928434853925, 0.2510928434853925, 0.2401556615138524, 0.2296573637153073, 0.21962767025150254, 0.21009896926797608, 0.20110619927478804, 0.19268653734486382, 0.18487882622158372, 0.1777226720373576, 0.17125715641406813, 0.16551913914067518, 0.16054118533450362, 0.15634923267877057, 0.15296020779851646, 0.15037988115495657, 0.1486012841616015, 0.14760397284848523, 0.14735430340072547, 0.14780671189307767, 0.14890581414586726, 0.15058901548290318, 0.15278927648771473, 0.15543771871404774, 0.15846584612550016, 0.16180726687440725, 0.16539889573485103, 0.16918168433697292, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 'PermShkCount': 7, 'TranShkStd': [0.3528101356710064, 0.3528101356710064, 0.3553133361361021, 0.3570497985321185, 0.358062647002616, 0.3583899558240917, 0.3580655727131512, 0.35711977391104305, 0.3555797914147738, 0.3534702416818744, 0.3508134774404022, 0.3476298787783864, 0.3439380957540673, 0.33975525189104816, 0.335097115786856, 0.32997824645518803, 0.32441211679253057, 0.318411218603437, 0.31198715185906833, 0.305150700243307, 0.2979118945148286, 0.2902800647440016, 0.28226388203493175, 0.27387138987799703, 0.2651100247519064, 0.25598662494655583, 0.24650742572135614, 0.23667803771374105, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 'TranShkCount': 7, 'UnempPrb': 0.05, 'UnempPrbRet': 0.005, 'IncUnemp': 0.3, 'IncUnempRet': 0.0, 'tax_rate': 0.0, 'T_retire': 0, 'vFuncBool': False, 'CubicBool': False, 'neutral_measure': False, 'NewbornTransShk': False, 'P0': 21.397337769996003}\n",
+                        "\n",
+                        "\n",
+                        "PermGroFac\n",
+                        "current:  [1.0434056222652845, 1.0399264609207084, 1.0365832831214161, 1.033374850583594, 1.030299977365458, 1.02735752913683, 1.0245464224818346, 1.0218656242341422, 1.019314150844187, 1.0168910677778014, 1.0145954889457791, 1.0124265761638418, 1.0103835386425875, 1.0084656325069654, 1.0066721603448634, 1.0050024707844432, 1.0034559580998452, 1.0020320618449452, 1.0007302665148479, 0.9995501012348152, 0.9984911394763908, 0.9975529988004792, 0.996735340627129, 0.99603787003188, 0.9954603355684468, 0.9950025291176404, 0.9946642857623696, 0.9944454836886139, 0.9943460441123051, 0.9943659312320589, 0.9945051522076688, 0.9947637571644364, 0.995141839223195, 0.9956395345562754, 0.9962570224691691, 0.9969945255082701, 0.9978523095944938, 0.9988306841831407, 0.9999300024499419, 1.001150661503553, 0.6821, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
+                        "original: [1.0419577244168425, 1.042054094671763, 1.0419741368106594, 1.0417252594892359, 1.0413149489133517, 1.0407507570649852, 1.0400402904123685, 1.0391911991207905, 1.038211166778343, 1.037107900648818, 1.0358891224620352, 1.0345625597498684, 1.033135937734556, 1.0316169717741348, 1.0300133603683226, 1.0283327787265384, 1.026582872898661, 1.0247712544676226, 1.0229054958019392, 1.02099312586525, 1.0190416265789661, 1.0170584297334835, 1.0150509144425075, 1.0130264051347238, 1.0109921700764062, 1.0089554204182813, 1.0069233097596944, 1.004902934223072, 1.0029013330314762, 1.000925489582226, 0.9989823330098357, 0.9970787402313407, 0.9952215384681313, 0.9934175082380967, 0.9916733868131232, 0.9899958721369805, 0.9883916271999132, 0.9868672848666431, 0.9854294531556296, 0.9840847209682294, 0.5719733840877946, 0.9892378656535716, 0.9892378656535721, 0.9892378656535716, 0.9892378656535716, 0.9892378656535716, 0.9892378656535716, 0.9892378656535721, 0.9892378656535716, 0.9892378656535721, 0.9892378656535716, 0.9892378656535716, 0.9892378656535721, 0.9892378656535712, 0.9892378656535721, 0.9892378656535721, 0.9892378656535716, 0.9892378656535712, 0.9892378656535721, 0.9892378656535716, 0.9892378656535721, 0.9892378656535716, 0.9892378656535716, 0.9892378656535716, 0.9892378656535721]\n",
+                        "\n",
+                        "\n",
+                        "aXtraNestFac\n",
+                        "current:  1\n",
+                        "original: 3\n",
+                        "\n",
+                        "\n",
+                        "TranShkStd\n",
+                        "current:  [0.3528101356710064, 0.3528101356710064, 0.3553133361361021, 0.3570497985321185, 0.358062647002616, 0.3583899558240917, 0.3580655727131512, 0.35711977391104305, 0.3555797914147738, 0.3534702416818744, 0.3508134774404022, 0.3476298787783864, 0.3439380957540673, 0.33975525189104816, 0.335097115786856, 0.32997824645518803, 0.32441211679253057, 0.318411218603437, 0.31198715185906833, 0.305150700243307, 0.2979118945148286, 0.2902800647440016, 0.28226388203493175, 0.27387138987799703, 0.2651100247519064, 0.25598662494655583, 0.24650742572135614, 0.23667803771374105, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
+                        "original: [0.3528101356710064, 0.3528101356710064, 0.3553133361361021, 0.3570497985321185, 0.358062647002616, 0.3583899558240917, 0.3580655727131512, 0.35711977391104305, 0.3555797914147738, 0.3534702416818744, 0.3508134774404022, 0.3476298787783864, 0.3439380957540673, 0.33975525189104816, 0.335097115786856, 0.32997824645518803, 0.32441211679253057, 0.318411218603437, 0.31198715185906833, 0.305150700243307, 0.2979118945148286, 0.2902800647440016, 0.28226388203493175, 0.27387138987799703, 0.2651100247519064, 0.25598662494655583, 0.24650742572135614, 0.23667803771374105, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
+                        "\n",
+                        "\n",
+                        "PermShkStd\n",
+                        "current:  [0.2510928434853925, 0.2510928434853925, 0.2401556615138524, 0.2296573637153073, 0.21962767025150254, 0.21009896926797608, 0.20110619927478804, 0.19268653734486382, 0.18487882622158372, 0.1777226720373576, 0.17125715641406813, 0.16551913914067518, 0.16054118533450362, 0.15634923267877057, 0.15296020779851646, 0.15037988115495657, 0.1486012841616015, 0.14760397284848523, 0.14735430340072547, 0.14780671189307767, 0.14890581414586726, 0.15058901548290318, 0.15278927648771473, 0.15543771871404774, 0.15846584612550016, 0.16180726687440725, 0.16539889573485103, 0.16918168433697292, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
+                        "original: [0.2510928434853925, 0.2510928434853925, 0.2401556615138524, 0.2296573637153073, 0.21962767025150254, 0.21009896926797608, 0.20110619927478804, 0.19268653734486382, 0.18487882622158372, 0.1777226720373576, 0.17125715641406813, 0.16551913914067518, 0.16054118533450362, 0.15634923267877057, 0.15296020779851646, 0.15037988115495657, 0.1486012841616015, 0.14760397284848523, 0.14735430340072547, 0.14780671189307767, 0.14890581414586726, 0.15058901548290318, 0.15278927648771473, 0.15543771871404774, 0.15846584612550016, 0.16180726687440725, 0.16539889573485103, 0.16918168433697292, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
+                        "\n",
+                        "\n",
+                        "P0\n",
+                        "current:  21.397337769996003\n",
+                        "original: 21.397337769996003\n",
+                        "\n",
+                        "\n",
+                        "seed\n",
+                        "current:  31382\n",
+                        "original: 0\n",
+                        "\n",
+                        "\n",
+                        "AgentCount\n",
+                        "current:  10000\n",
+                        "original: 10000\n",
+                        "\n",
+                        "\n",
+                        "Rfree\n",
+                        "current:  1.03\n",
+                        "original: 1.03\n",
+                        "\n",
+                        "\n",
+                        "tax_rate\n",
+                        "current:  0.0\n",
+                        "original: 0.0\n",
+                        "\n",
+                        "\n",
+                        "aXtraCount\n",
+                        "current:  200\n",
+                        "original: 48\n",
+                        "\n",
+                        "\n",
+                        "aXtraMin\n",
+                        "current:  0.001\n",
+                        "original: 0.001\n",
+                        "\n",
+                        "\n",
+                        "vFuncBool\n",
+                        "current:  False\n",
+                        "original: False\n",
+                        "\n",
+                        "\n",
+                        "IncShkDstn\n",
+                        "current:  <HARK.distribution.IndexDistribution object at 0x7f79be35dde0>\n",
+                        "original: <HARK.distribution.IndexDistribution object at 0x7f79be56fd00>\n",
+                        "\n",
+                        "\n",
+                        "UnempPrbRet\n",
+                        "current:  0.005\n",
+                        "original: 0.005\n",
+                        "\n",
+                        "\n",
+                        "state_prev\n",
+                        "current:  {'pLvl': None, 'PlvlAgg': None, 'bNrm': None, 'mNrm': None, 'aNrm': None, 'aLvl': None}\n",
+                        "original: {'pLvl': None, 'PlvlAgg': None, 'bNrm': None, 'mNrm': None, 'aNrm': None, 'aLvl': None}\n",
+                        "\n",
+                        "\n",
+                        "PermShkCount\n",
+                        "current:  7\n",
+                        "original: 7\n",
+                        "\n",
+                        "\n",
+                        "neutral_measure\n",
+                        "current:  False\n",
+                        "original: False\n",
+                        "\n",
+                        "\n",
+                        "tolerance\n",
+                        "current:  1e-06\n",
+                        "original: 1e-06\n",
+                        "\n",
+                        "\n",
+                        "time_vary\n",
+                        "current:  ['LivPrb', 'PermGroFac', 'IncShkDstn', 'PermShkDstn', 'TranShkDstn', 'DiscFac']\n",
+                        "original: ['LivPrb', 'PermGroFac', 'IncShkDstn', 'PermShkDstn', 'TranShkDstn', 'DiscFac']\n",
+                        "\n",
+                        "\n",
+                        "aXtraMax\n",
+                        "current:  100\n",
+                        "original: 20\n",
+                        "\n",
+                        "\n",
+                        "newborn_init_history\n",
+                        "current:  {}\n",
+                        "original: {}\n",
+                        "\n",
+                        "\n",
+                        "time_inv\n",
+                        "current:  ['CRRA', 'BoroCnstArt', 'BoroCnstArt', 'vFuncBool', 'CubicBool', 'Rfree', 'aXtraGrid']\n",
+                        "original: ['CRRA', 'BoroCnstArt', 'BoroCnstArt', 'vFuncBool', 'CubicBool', 'Rfree', 'aXtraGrid']\n",
+                        "\n",
+                        "\n",
+                        "PermShkDstn\n",
+                        "current:  <HARK.distribution.IndexDistribution object at 0x7f79be35f910>\n",
+                        "original: <HARK.distribution.IndexDistribution object at 0x7f79be3b9b10>\n",
+                        "\n",
+                        "\n",
+                        "aXtraGrid\n",
+                        "current:  [1.00000000e-03 2.44808926e-02 4.85125868e-02 7.31080029e-02\n",
+                        " 9.82803643e-02 1.24043205e-01 1.50410375e-01 1.77396052e-01\n",
+                        " 2.05014744e-01 2.33281299e-01 2.62210915e-01 2.91819146e-01\n",
+                        " 3.22121910e-01 3.53135499e-01 3.84876587e-01 4.17362240e-01\n",
+                        " 4.50609923e-01 4.84637511e-01 5.19463300e-01 5.55106012e-01\n",
+                        " 5.91584810e-01 6.28919308e-01 6.67129577e-01 7.06236161e-01\n",
+                        " 7.46260085e-01 7.87222868e-01 8.29146533e-01 8.72053619e-01\n",
+                        " 9.15967195e-01 9.60910872e-01 1.00690881e+00 1.05398574e+00\n",
+                        " 1.10216698e+00 1.15147843e+00 1.20194659e+00 1.25359861e+00\n",
+                        " 1.30646226e+00 1.36056595e+00 1.41593877e+00 1.47261050e+00\n",
+                        " 1.53061160e+00 1.58997325e+00 1.65072738e+00 1.71290665e+00\n",
+                        " 1.77654448e+00 1.84167509e+00 1.90833350e+00 1.97655555e+00\n",
+                        " 2.04637791e+00 2.11783812e+00 2.19097460e+00 2.26582668e+00\n",
+                        " 2.34243460e+00 2.42083955e+00 2.50108367e+00 2.58321011e+00\n",
+                        " 2.66726303e+00 2.75328762e+00 2.84133012e+00 2.93143787e+00\n",
+                        " 3.02365932e+00 3.11804405e+00 3.21464280e+00 3.31350751e+00\n",
+                        " 3.41469133e+00 3.51824867e+00 3.62423519e+00 3.73270789e+00\n",
+                        " 3.84372508e+00 3.95734644e+00 4.07363308e+00 4.19264750e+00\n",
+                        " 4.31445369e+00 4.43911714e+00 4.56670488e+00 4.69728550e+00\n",
+                        " 4.83092920e+00 4.96770785e+00 5.10769497e+00 5.25096583e+00\n",
+                        " 5.39759745e+00 5.54766868e+00 5.70126020e+00 5.85845457e+00\n",
+                        " 6.01933633e+00 6.18399195e+00 6.35250998e+00 6.52498101e+00\n",
+                        " 6.70149776e+00 6.88215514e+00 7.06705029e+00 7.25628260e+00\n",
+                        " 7.44995381e+00 7.64816806e+00 7.85103190e+00 8.05865441e+00\n",
+                        " 8.27114720e+00 8.48862454e+00 8.71120333e+00 8.93900326e+00\n",
+                        " 9.17214678e+00 9.41075926e+00 9.65496897e+00 9.90490721e+00\n",
+                        " 1.01607084e+01 1.04225100e+01 1.06904527e+01 1.09646808e+01\n",
+                        " 1.12453415e+01 1.15325858e+01 1.18265681e+01 1.21274465e+01\n",
+                        " 1.24353828e+01 1.27505424e+01 1.30730948e+01 1.34032135e+01\n",
+                        " 1.37410760e+01 1.40868638e+01 1.44407630e+01 1.48029636e+01\n",
+                        " 1.51736606e+01 1.55530532e+01 1.59413454e+01 1.63387459e+01\n",
+                        " 1.67454684e+01 1.71617316e+01 1.75877593e+01 1.80237804e+01\n",
+                        " 1.84700295e+01 1.89267465e+01 1.93941768e+01 1.98725719e+01\n",
+                        " 2.03621889e+01 2.08632911e+01 2.13761478e+01 2.19010348e+01\n",
+                        " 2.24382344e+01 2.29880353e+01 2.35507330e+01 2.41266303e+01\n",
+                        " 2.47160366e+01 2.53192688e+01 2.59366514e+01 2.65685161e+01\n",
+                        " 2.72152028e+01 2.78770591e+01 2.85544408e+01 2.92477122e+01\n",
+                        " 2.99572460e+01 3.06834236e+01 3.14266354e+01 3.21872811e+01\n",
+                        " 3.29657696e+01 3.37625195e+01 3.45779590e+01 3.54125267e+01\n",
+                        " 3.62666712e+01 3.71408517e+01 3.80355382e+01 3.89512119e+01\n",
+                        " 3.98883648e+01 4.08475010e+01 4.18291360e+01 4.28337977e+01\n",
+                        " 4.38620262e+01 4.49143743e+01 4.59914077e+01 4.70937056e+01\n",
+                        " 4.82218606e+01 4.93764792e+01 5.05581822e+01 5.17676049e+01\n",
+                        " 5.30053976e+01 5.42722257e+01 5.55687704e+01 5.68957286e+01\n",
+                        " 5.82538139e+01 5.96437564e+01 6.10663034e+01 6.25222197e+01\n",
+                        " 6.40122881e+01 6.55373096e+01 6.70981042e+01 6.86955111e+01\n",
+                        " 7.03303890e+01 7.20036170e+01 7.37160946e+01 7.54687426e+01\n",
+                        " 7.72625032e+01 7.90983407e+01 8.09772424e+01 8.29002182e+01\n",
+                        " 8.48683022e+01 8.68825523e+01 8.89440516e+01 9.10539083e+01\n",
+                        " 9.32132569e+01 9.54232583e+01 9.76851006e+01 1.00000000e+02]\n",
+                        "original: [1.00000000e-03 2.01713727e-02 4.04645973e-02 6.19689346e-02\n",
+                        " 8.47826891e-02 1.09014323e-01 1.34783729e-01 1.62223697e-01\n",
+                        " 1.91481594e-01 2.22721307e-01 2.56125489e-01 2.91898165e-01\n",
+                        " 3.30267760e-01 3.71490637e-01 4.15855231e-01 4.63686907e-01\n",
+                        " 5.15353678e-01 5.71272965e-01 6.31919613e-01 6.97835428e-01\n",
+                        " 7.69640582e-01 8.48047286e-01 9.33876256e-01 1.02807664e+00\n",
+                        " 1.13175022e+00 1.24618095e+00 1.37287121e+00 1.51358644e+00\n",
+                        " 1.67041051e+00 1.84581461e+00 2.04274370e+00 2.26472534e+00\n",
+                        " 2.51600777e+00 2.80173618e+00 3.12817922e+00 3.50302228e+00\n",
+                        " 3.93574988e+00 4.43814835e+00 5.02497206e+00 5.71483401e+00\n",
+                        " 6.53140746e+00 7.50506263e+00 8.67511887e+00 1.00929770e+01\n",
+                        " 1.18265253e+01 1.39664114e+01 1.66350835e+01 2.00000000e+01]\n",
+                        "\n",
+                        "\n",
+                        "aNrmInitMean\n",
+                        "current:  -0.928110551297082\n",
+                        "original: -0.928110551297082\n",
+                        "\n",
+                        "\n",
+                        "MaxKinks\n",
+                        "current:  400\n",
+                        "original: 400\n",
+                        "\n",
+                        "\n",
+                        "solve_one_period\n",
+                        "current:  <function make_one_period_oo_solver.<locals>.one_period_solver at 0x7f79be31a3b0>\n",
+                        "original: <function make_one_period_oo_solver.<locals>.one_period_solver at 0x7f79be3d0550>\n",
+                        "\n",
+                        "\n",
+                        "PermGroFacAgg\n",
+                        "current:  1.0\n",
+                        "original: 1.0\n",
+                        "\n",
+                        "\n",
+                        "T_cycle\n",
+                        "current:  65\n",
+                        "original: 65\n",
+                        "\n",
+                        "\n",
+                        "CRRA\n",
+                        "current:  1.3759978446748666\n",
+                        "original: 2.0\n",
+                        "\n",
+                        "\n",
+                        "BoroCnstArt\n",
+                        "current:  0.0\n",
+                        "original: 0.0\n",
+                        "\n",
+                        "\n",
+                        "TranShkDstn\n",
+                        "current:  <HARK.distribution.IndexDistribution object at 0x7f7abc5a3a90>\n",
+                        "original: <HARK.distribution.IndexDistribution object at 0x7f79be3bbc10>\n",
+                        "\n",
+                        "\n",
+                        "read_shocks\n",
+                        "current:  False\n",
+                        "original: False\n",
+                        "\n",
+                        "\n",
+                        "cycles\n",
+                        "current:  1\n",
+                        "original: 1\n",
+                        "\n",
+                        "\n",
+                        "shock_vars\n",
+                        "current:  ['PermShk', 'TranShk']\n",
+                        "original: ['PermShk', 'TranShk']\n",
+                        "\n",
+                        "\n",
+                        "aNrmInitStd\n",
+                        "current:  1.6577133299830675\n",
+                        "original: 1.6577133299830675\n",
+                        "\n",
+                        "\n",
+                        "UnempPrb\n",
+                        "current:  0.05\n",
+                        "original: 0.05\n",
+                        "\n",
+                        "\n",
+                        "RNG\n",
+                        "current:  Generator(PCG64)\n",
+                        "original: Generator(PCG64)\n",
+                        "\n",
+                        "\n",
+                        "aXtraExtra\n",
+                        "current:  [None, None]\n",
+                        "original: [None]\n",
+                        "\n",
+                        "\n",
+                        "pLvlInitMean\n",
+                        "current:  3.0632665110178623\n",
+                        "original: 3.0632665110178623\n",
+                        "\n",
+                        "\n",
+                        "DiscFac\n",
+                        "current:  [0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122]\n",
+                        "original: [0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96]\n",
+                        "\n",
+                        "\n",
+                        "pseudo_terminal\n",
+                        "current:  False\n",
+                        "original: False\n",
+                        "\n",
+                        "\n",
+                        "pLvlInitStd\n",
+                        "current:  0.758541123818503\n",
+                        "original: 0.758541123818503\n",
+                        "\n",
+                        "\n",
+                        "T_retire\n",
+                        "current:  39\n",
+                        "original: 0\n",
+                        "\n",
+                        "\n",
+                        "verbose\n",
+                        "current:  1\n",
+                        "original: 1\n",
+                        "\n",
+                        "\n",
+                        "history\n",
+                        "current:  {}\n",
+                        "original: {}\n",
+                        "\n",
+                        "\n",
+                        "LivPrb\n",
+                        "current:  [0.998341, 0.998262, 0.99826, 0.998172, 0.99803, 0.99796, 0.997886, 0.997792, 0.997587, 0.99747, 0.997398, 0.997621, 0.997822, 0.997755, 0.997607, 0.997421, 0.99722, 0.996942, 0.996701, 0.996562, 0.996243, 0.996023, 0.995789, 0.995449, 0.995122, 0.994844, 0.994377, 0.993913, 0.993402, 0.992824, 0.992191, 0.991511, 0.990844, 0.990081, 0.989317, 0.988495, 0.987654, 0.986892, 0.986244, 0.985647, 0.984987, 0.984198, 0.983305, 0.982293, 0.981146, 0.979812, 0.97829, 0.976614, 0.974779, 0.972732, 0.970243, 0.967372, 0.964395, 0.9614, 0.958184, 0.954529, 0.950045, 0.944392, 0.937261, 0.928746, 0.9191320000000001, 0.908706, 0.897671, 0.886107, 0.873964]\n",
+                        "original: [0.998566, 0.998583, 0.998599, 0.998609, 0.998611, 0.99861, 0.998601, 0.998569, 0.998508, 0.998419, 0.998312, 0.998192, 0.998056, 0.997906, 0.99774, 0.997556, 0.997348, 0.997115, 0.996852, 0.996562, 0.996249, 0.995916, 0.995561, 0.995186, 0.99479, 0.994349, 0.993881, 0.993428, 0.993005, 0.992583, 0.992124, 0.991583, 0.990942, 0.990175, 0.98929, 0.988296, 0.987216, 0.986059, 0.984831, 0.983509, 0.982022, 0.980368, 0.978602, 0.976732, 0.974708, 0.97243, 0.969863, 0.967036, 0.963933, 0.960506, 0.956589, 0.952211, 0.947534, 0.942585, 0.937209, 0.931163, 0.924276, 0.916534, 0.907855, 0.898197, 0.887532, 0.8758360000000001, 0.863084, 0.849246, 0.834296]\n",
+                        "\n",
+                        "\n",
+                        "solution_terminal\n",
+                        "current:  <HARK.ConsumptionSaving.ConsIndShockModel.ConsumerSolution object at 0x7f79be35ff70>\n",
+                        "original: <HARK.ConsumptionSaving.ConsIndShockModel.ConsumerSolution object at 0x7f79be56fcd0>\n",
+                        "\n",
+                        "\n",
+                        "IncUnempRet\n",
+                        "current:  0.0\n",
+                        "original: 0.0\n",
+                        "\n",
+                        "\n",
+                        "NewbornTransShk\n",
+                        "current:  False\n",
+                        "original: False\n",
+                        "\n",
+                        "\n",
+                        "shock_history\n",
+                        "current:  {}\n",
+                        "original: {}\n",
+                        "\n",
+                        "\n",
+                        "CubicBool\n",
+                        "current:  False\n",
+                        "original: False\n",
+                        "\n",
+                        "\n",
+                        "TranShkCount\n",
+                        "current:  7\n",
+                        "original: 7\n",
+                        "\n",
+                        "\n",
+                        "IncUnemp\n",
+                        "current:  0.3\n",
+                        "original: 0.3\n",
+                        "\n",
+                        "\n"
+                    ]
+                }
+            ],
+            "source": [
+                "param_list = set(lifecycle_agent.__dict__.keys()).union(\n",
+                "    set(indshk_agent.__dict__.keys())\n",
+                ")\n",
+                "\n",
+                "for param in param_list:\n",
+                "    current_value = getattr(indshk_agent, param, None)\n",
+                "    original_value = getattr(lifecycle_agent, param, None)\n",
+                "\n",
+                "    if current_value is None:\n",
+                "        print(f\"{param} not in indshk_agent\\n\")\n",
+                "    elif original_value is None:\n",
+                "        print(f\"{param} not in lifecycle_agent\\n\")\n",
+                "    else:\n",
+                "        print(f\"{param}\")\n",
+                "        print(f\"current:  {current_value}\")\n",
+                "        print(f\"original: {original_value}\")\n",
+                "        print(\"\\n\")"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 11,
+            "metadata": {},
+            "outputs": [
+                {
+                    "data": {
+                        "image/png": 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",
+                        "text/plain": [
+                            "<Figure size 640x480 with 1 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                }
+            ],
+            "source": [
+                "lifecycle_agent.solve()\n",
+                "# Set up the variables we want to keep track of.\n",
+                "lifecycle_agent.track_vars = [\"aNrm\", \"cNrm\", \"pLvl\", \"t_age\", \"mNrm\"]\n",
+                "\n",
+                "lifecycle_agent.T_sim = 200\n",
+                "# Run the simulations\n",
+                "lifecycle_agent.initialize_sim()\n",
+                "history = lifecycle_agent.simulate()\n",
+                "\n",
+                "raw_data = {\n",
+                "    \"Age\": lifecycle_agent.history[\"t_age\"].flatten() + 25 - 1,\n",
+                "    \"pIncome\": lifecycle_agent.history[\"pLvl\"].flatten(),\n",
+                "    \"nrmM\": lifecycle_agent.history[\"mNrm\"].flatten(),\n",
+                "    \"nrmC\": lifecycle_agent.history[\"cNrm\"].flatten(),\n",
+                "}\n",
+                "\n",
+                "Data = pd.DataFrame(raw_data)\n",
+                "Data[\"Cons\"] = Data.nrmC * Data.pIncome\n",
+                "Data[\"M\"] = Data.nrmM * Data.pIncome\n",
+                "\n",
+                "# Find the mean of each variable at every age\n",
+                "AgeMeans = Data.groupby([\"Age\"]).median().reset_index()\n",
+                "\n",
+                "plt.figure()\n",
+                "plt.plot(AgeMeans.Age, AgeMeans.pIncome, label=\"Permanent Income\")\n",
+                "plt.plot(AgeMeans.Age, AgeMeans.M, label=\"Market resources\")\n",
+                "plt.plot(AgeMeans.Age, AgeMeans.Cons, label=\"Consumption\")\n",
+                "plt.legend()\n",
+                "plt.xlabel(\"Age\")\n",
+                "plt.ylabel(\"Thousands of USD\")\n",
+                "plt.title(\"Variable Medians Conditional on Survival\")\n",
+                "plt.grid()"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
+            "outputs": [],
+            "source": []
+        }
+    ],
+    "metadata": {
+        "kernelspec": {
+            "display_name": "Python 3 (ipykernel)",
+            "language": "python",
+            "name": "python3"
+        },
+        "language_info": {
+            "codemirror_mode": {
+                "name": "ipython",
+                "version": 3
+            },
+            "file_extension": ".py",
+            "mimetype": "text/x-python",
+            "name": "python",
+            "nbconvert_exporter": "python",
+            "pygments_lexer": "ipython3",
+            "version": "3.10.13"
+        }
     },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 640x480 with 0 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_funcs([sol.cFunc for sol in indshk_agent.solution[:-1:5]], 0, 20)\n",
-    "plt.savefig(\"../content/figures/IndShock_cFunc.png\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Set up the variables we want to keep track of.\n",
-    "indshk_agent.track_vars = [\"aNrm\", \"cNrm\", \"pLvl\", \"t_age\", \"mNrm\"]\n",
-    "\n",
-    "indshk_agent.T_sim = 200\n",
-    "# Run the simulations\n",
-    "indshk_agent.initialize_sim()\n",
-    "history = indshk_agent.simulate()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "raw_data = {\n",
-    "    \"Age\": indshk_agent.history[\"t_age\"].flatten() + 25 - 1,\n",
-    "    \"pIncome\": indshk_agent.history[\"pLvl\"].flatten(),\n",
-    "    \"nrmM\": indshk_agent.history[\"mNrm\"].flatten(),\n",
-    "    \"nrmC\": indshk_agent.history[\"cNrm\"].flatten(),\n",
-    "}\n",
-    "\n",
-    "Data = pd.DataFrame(raw_data)\n",
-    "Data[\"Cons\"] = Data.nrmC * Data.pIncome\n",
-    "Data[\"M\"] = Data.nrmM * Data.pIncome"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Find the mean of each variable at every age\n",
-    "AgeMeans = Data.groupby([\"Age\"]).median().reset_index()\n",
-    "\n",
-    "plt.figure()\n",
-    "plt.plot(AgeMeans.Age, AgeMeans.pIncome, label=\"Permanent Income\")\n",
-    "plt.plot(AgeMeans.Age, AgeMeans.M, label=\"Market resources\")\n",
-    "plt.plot(AgeMeans.Age, AgeMeans.Cons, label=\"Consumption\")\n",
-    "plt.legend()\n",
-    "plt.xlabel(\"Age\")\n",
-    "plt.ylabel(\"Thousands of USD\")\n",
-    "plt.title(\"Variable Medians Conditional on Survival\")\n",
-    "plt.grid()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "indshk_agent = IndShkLifeCycleConsumerType(**parameters.init_consumer_objects)\n",
-    "indshk_agent.CRRA = CRRA\n",
-    "indshk_agent.DiscFac = [b * DiscFacAdj for b in parameters.DiscFac_timevary]\n",
-    "\n",
-    "lifecycle_agent = IndShkLifeCycleConsumerType(\n",
-    "    **{**init_lifecycle, \"PermGroFacAgg\": 1.0}\n",
-    ")\n",
-    "\n",
-    "\n",
-    "lifecycle_agent.DiscFac = [\n",
-    "    init_lifecycle[\"DiscFac\"] for b in parameters.DiscFac_timevary\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "PerfMITShk\n",
-      "current:  False\n",
-      "original: False\n",
-      "\n",
-      "\n",
-      "bilt\n",
-      "current:  {}\n",
-      "original: {}\n",
-      "\n",
-      "\n",
-      "quiet\n",
-      "current:  False\n",
-      "original: False\n",
-      "\n",
-      "\n",
-      "state_now\n",
-      "current:  {'pLvl': None, 'PlvlAgg': None, 'bNrm': None, 'mNrm': None, 'aNrm': None, 'aLvl': None}\n",
-      "original: {'pLvl': None, 'PlvlAgg': None, 'bNrm': None, 'mNrm': None, 'aNrm': None, 'aLvl': None}\n",
-      "\n",
-      "\n",
-      "T_age\n",
-      "current:  65\n",
-      "original: 65\n",
-      "\n",
-      "\n",
-      "controls\n",
-      "current:  {}\n",
-      "original: {}\n",
-      "\n",
-      "\n",
-      "shocks\n",
-      "current:  {}\n",
-      "original: {}\n",
-      "\n",
-      "\n",
-      "track_vars\n",
-      "current:  []\n",
-      "original: []\n",
-      "\n",
-      "\n",
-      "parameters\n",
-      "current:  {'cycles': 1, 'CRRA': 5.0, 'Rfree': 1.03, 'DiscFac': array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
-      "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
-      "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
-      "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]), 'LivPrb': [0.998341, 0.998262, 0.99826, 0.998172, 0.99803, 0.99796, 0.997886, 0.997792, 0.997587, 0.99747, 0.997398, 0.997621, 0.997822, 0.997755, 0.997607, 0.997421, 0.99722, 0.996942, 0.996701, 0.996562, 0.996243, 0.996023, 0.995789, 0.995449, 0.995122, 0.994844, 0.994377, 0.993913, 0.993402, 0.992824, 0.992191, 0.991511, 0.990844, 0.990081, 0.989317, 0.988495, 0.987654, 0.986892, 0.986244, 0.985647, 0.984987, 0.984198, 0.983305, 0.982293, 0.981146, 0.979812, 0.97829, 0.976614, 0.974779, 0.972732, 0.970243, 0.967372, 0.964395, 0.9614, 0.958184, 0.954529, 0.950045, 0.944392, 0.937261, 0.928746, 0.9191320000000001, 0.908706, 0.897671, 0.886107, 0.873964], 'PermGroFac': [1.0434056222652845, 1.0399264609207084, 1.0365832831214161, 1.033374850583594, 1.030299977365458, 1.02735752913683, 1.0245464224818346, 1.0218656242341422, 1.019314150844187, 1.0168910677778014, 1.0145954889457791, 1.0124265761638418, 1.0103835386425875, 1.0084656325069654, 1.0066721603448634, 1.0050024707844432, 1.0034559580998452, 1.0020320618449452, 1.0007302665148479, 0.9995501012348152, 0.9984911394763908, 0.9975529988004792, 0.996735340627129, 0.99603787003188, 0.9954603355684468, 0.9950025291176404, 0.9946642857623696, 0.9944454836886139, 0.9943460441123051, 0.9943659312320589, 0.9945051522076688, 0.9947637571644364, 0.995141839223195, 0.9956395345562754, 0.9962570224691691, 0.9969945255082701, 0.9978523095944938, 0.9988306841831407, 0.9999300024499419, 1.001150661503553, 0.6821, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], 'BoroCnstArt': 0.0, 'MaxKinks': 400, 'AgentCount': 10000, 'aNrmInitMean': -0.928110551297082, 'aNrmInitStd': 1.6577133299830675, 'pLvlInitMean': 3.0632665110178623, 'pLvlInitStd': 0.758541123818503, 'PermGroFacAgg': 1.0, 'T_age': 65, 'T_cycle': 65, 'PerfMITShk': False, 'aXtraMin': 0.001, 'aXtraMax': 100, 'aXtraNestFac': 1, 'aXtraCount': 200, 'aXtraExtra': [None, None], 'PermShkStd': [0.2510928434853925, 0.2510928434853925, 0.2401556615138524, 0.2296573637153073, 0.21962767025150254, 0.21009896926797608, 0.20110619927478804, 0.19268653734486382, 0.18487882622158372, 0.1777226720373576, 0.17125715641406813, 0.16551913914067518, 0.16054118533450362, 0.15634923267877057, 0.15296020779851646, 0.15037988115495657, 0.1486012841616015, 0.14760397284848523, 0.14735430340072547, 0.14780671189307767, 0.14890581414586726, 0.15058901548290318, 0.15278927648771473, 0.15543771871404774, 0.15846584612550016, 0.16180726687440725, 0.16539889573485103, 0.16918168433697292, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 'PermShkCount': 7, 'TranShkStd': [0.3528101356710064, 0.3528101356710064, 0.3553133361361021, 0.3570497985321185, 0.358062647002616, 0.3583899558240917, 0.3580655727131512, 0.35711977391104305, 0.3555797914147738, 0.3534702416818744, 0.3508134774404022, 0.3476298787783864, 0.3439380957540673, 0.33975525189104816, 0.335097115786856, 0.32997824645518803, 0.32441211679253057, 0.318411218603437, 0.31198715185906833, 0.305150700243307, 0.2979118945148286, 0.2902800647440016, 0.28226388203493175, 0.27387138987799703, 0.2651100247519064, 0.25598662494655583, 0.24650742572135614, 0.23667803771374105, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 'TranShkCount': 7, 'UnempPrb': 0.05, 'UnempPrbRet': 0.005, 'IncUnemp': 0.3, 'IncUnempRet': 0.0, 'tax_rate': 0.0, 'T_retire': 39, 'vFuncBool': False, 'CubicBool': False, 'neutral_measure': False, 'NewbornTransShk': False, 'P0': 21.397337769996003}\n",
-      "original: {'cycles': 1, 'CRRA': 2.0, 'Rfree': 1.03, 'DiscFac': 0.96, 'LivPrb': [0.998566, 0.998583, 0.998599, 0.998609, 0.998611, 0.99861, 0.998601, 0.998569, 0.998508, 0.998419, 0.998312, 0.998192, 0.998056, 0.997906, 0.99774, 0.997556, 0.997348, 0.997115, 0.996852, 0.996562, 0.996249, 0.995916, 0.995561, 0.995186, 0.99479, 0.994349, 0.993881, 0.993428, 0.993005, 0.992583, 0.992124, 0.991583, 0.990942, 0.990175, 0.98929, 0.988296, 0.987216, 0.986059, 0.984831, 0.983509, 0.982022, 0.980368, 0.978602, 0.976732, 0.974708, 0.97243, 0.969863, 0.967036, 0.963933, 0.960506, 0.956589, 0.952211, 0.947534, 0.942585, 0.937209, 0.931163, 0.924276, 0.916534, 0.907855, 0.898197, 0.887532, 0.8758360000000001, 0.863084, 0.849246, 0.834296], 'PermGroFac': [1.0419577244168425, 1.042054094671763, 1.0419741368106594, 1.0417252594892359, 1.0413149489133517, 1.0407507570649852, 1.0400402904123685, 1.0391911991207905, 1.038211166778343, 1.037107900648818, 1.0358891224620352, 1.0345625597498684, 1.033135937734556, 1.0316169717741348, 1.0300133603683226, 1.0283327787265384, 1.026582872898661, 1.0247712544676226, 1.0229054958019392, 1.02099312586525, 1.0190416265789661, 1.0170584297334835, 1.0150509144425075, 1.0130264051347238, 1.0109921700764062, 1.0089554204182813, 1.0069233097596944, 1.004902934223072, 1.0029013330314762, 1.000925489582226, 0.9989823330098357, 0.9970787402313407, 0.9952215384681313, 0.9934175082380967, 0.9916733868131232, 0.9899958721369805, 0.9883916271999132, 0.9868672848666431, 0.9854294531556296, 0.9840847209682294, 0.5719733840877946, 0.9892378656535716, 0.9892378656535721, 0.9892378656535716, 0.9892378656535716, 0.9892378656535716, 0.9892378656535716, 0.9892378656535721, 0.9892378656535716, 0.9892378656535721, 0.9892378656535716, 0.9892378656535716, 0.9892378656535721, 0.9892378656535712, 0.9892378656535721, 0.9892378656535721, 0.9892378656535716, 0.9892378656535712, 0.9892378656535721, 0.9892378656535716, 0.9892378656535721, 0.9892378656535716, 0.9892378656535716, 0.9892378656535716, 0.9892378656535721], 'BoroCnstArt': 0.0, 'MaxKinks': 400, 'AgentCount': 10000, 'aNrmInitMean': -0.928110551297082, 'aNrmInitStd': 1.6577133299830675, 'pLvlInitMean': 3.0632665110178623, 'pLvlInitStd': 0.758541123818503, 'PermGroFacAgg': 1.0, 'T_age': 65, 'T_cycle': 65, 'PerfMITShk': False, 'aXtraMin': 0.001, 'aXtraMax': 20, 'aXtraNestFac': 3, 'aXtraCount': 48, 'aXtraExtra': [None], 'PermShkStd': [0.2510928434853925, 0.2510928434853925, 0.2401556615138524, 0.2296573637153073, 0.21962767025150254, 0.21009896926797608, 0.20110619927478804, 0.19268653734486382, 0.18487882622158372, 0.1777226720373576, 0.17125715641406813, 0.16551913914067518, 0.16054118533450362, 0.15634923267877057, 0.15296020779851646, 0.15037988115495657, 0.1486012841616015, 0.14760397284848523, 0.14735430340072547, 0.14780671189307767, 0.14890581414586726, 0.15058901548290318, 0.15278927648771473, 0.15543771871404774, 0.15846584612550016, 0.16180726687440725, 0.16539889573485103, 0.16918168433697292, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 'PermShkCount': 7, 'TranShkStd': [0.3528101356710064, 0.3528101356710064, 0.3553133361361021, 0.3570497985321185, 0.358062647002616, 0.3583899558240917, 0.3580655727131512, 0.35711977391104305, 0.3555797914147738, 0.3534702416818744, 0.3508134774404022, 0.3476298787783864, 0.3439380957540673, 0.33975525189104816, 0.335097115786856, 0.32997824645518803, 0.32441211679253057, 0.318411218603437, 0.31198715185906833, 0.305150700243307, 0.2979118945148286, 0.2902800647440016, 0.28226388203493175, 0.27387138987799703, 0.2651100247519064, 0.25598662494655583, 0.24650742572135614, 0.23667803771374105, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 'TranShkCount': 7, 'UnempPrb': 0.05, 'UnempPrbRet': 0.005, 'IncUnemp': 0.3, 'IncUnempRet': 0.0, 'tax_rate': 0.0, 'T_retire': 0, 'vFuncBool': False, 'CubicBool': False, 'neutral_measure': False, 'NewbornTransShk': False, 'P0': 21.397337769996003}\n",
-      "\n",
-      "\n",
-      "PermGroFac\n",
-      "current:  [1.0434056222652845, 1.0399264609207084, 1.0365832831214161, 1.033374850583594, 1.030299977365458, 1.02735752913683, 1.0245464224818346, 1.0218656242341422, 1.019314150844187, 1.0168910677778014, 1.0145954889457791, 1.0124265761638418, 1.0103835386425875, 1.0084656325069654, 1.0066721603448634, 1.0050024707844432, 1.0034559580998452, 1.0020320618449452, 1.0007302665148479, 0.9995501012348152, 0.9984911394763908, 0.9975529988004792, 0.996735340627129, 0.99603787003188, 0.9954603355684468, 0.9950025291176404, 0.9946642857623696, 0.9944454836886139, 0.9943460441123051, 0.9943659312320589, 0.9945051522076688, 0.9947637571644364, 0.995141839223195, 0.9956395345562754, 0.9962570224691691, 0.9969945255082701, 0.9978523095944938, 0.9988306841831407, 0.9999300024499419, 1.001150661503553, 0.6821, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
-      "original: [1.0419577244168425, 1.042054094671763, 1.0419741368106594, 1.0417252594892359, 1.0413149489133517, 1.0407507570649852, 1.0400402904123685, 1.0391911991207905, 1.038211166778343, 1.037107900648818, 1.0358891224620352, 1.0345625597498684, 1.033135937734556, 1.0316169717741348, 1.0300133603683226, 1.0283327787265384, 1.026582872898661, 1.0247712544676226, 1.0229054958019392, 1.02099312586525, 1.0190416265789661, 1.0170584297334835, 1.0150509144425075, 1.0130264051347238, 1.0109921700764062, 1.0089554204182813, 1.0069233097596944, 1.004902934223072, 1.0029013330314762, 1.000925489582226, 0.9989823330098357, 0.9970787402313407, 0.9952215384681313, 0.9934175082380967, 0.9916733868131232, 0.9899958721369805, 0.9883916271999132, 0.9868672848666431, 0.9854294531556296, 0.9840847209682294, 0.5719733840877946, 0.9892378656535716, 0.9892378656535721, 0.9892378656535716, 0.9892378656535716, 0.9892378656535716, 0.9892378656535716, 0.9892378656535721, 0.9892378656535716, 0.9892378656535721, 0.9892378656535716, 0.9892378656535716, 0.9892378656535721, 0.9892378656535712, 0.9892378656535721, 0.9892378656535721, 0.9892378656535716, 0.9892378656535712, 0.9892378656535721, 0.9892378656535716, 0.9892378656535721, 0.9892378656535716, 0.9892378656535716, 0.9892378656535716, 0.9892378656535721]\n",
-      "\n",
-      "\n",
-      "aXtraNestFac\n",
-      "current:  1\n",
-      "original: 3\n",
-      "\n",
-      "\n",
-      "TranShkStd\n",
-      "current:  [0.3528101356710064, 0.3528101356710064, 0.3553133361361021, 0.3570497985321185, 0.358062647002616, 0.3583899558240917, 0.3580655727131512, 0.35711977391104305, 0.3555797914147738, 0.3534702416818744, 0.3508134774404022, 0.3476298787783864, 0.3439380957540673, 0.33975525189104816, 0.335097115786856, 0.32997824645518803, 0.32441211679253057, 0.318411218603437, 0.31198715185906833, 0.305150700243307, 0.2979118945148286, 0.2902800647440016, 0.28226388203493175, 0.27387138987799703, 0.2651100247519064, 0.25598662494655583, 0.24650742572135614, 0.23667803771374105, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
-      "original: [0.3528101356710064, 0.3528101356710064, 0.3553133361361021, 0.3570497985321185, 0.358062647002616, 0.3583899558240917, 0.3580655727131512, 0.35711977391104305, 0.3555797914147738, 0.3534702416818744, 0.3508134774404022, 0.3476298787783864, 0.3439380957540673, 0.33975525189104816, 0.335097115786856, 0.32997824645518803, 0.32441211679253057, 0.318411218603437, 0.31198715185906833, 0.305150700243307, 0.2979118945148286, 0.2902800647440016, 0.28226388203493175, 0.27387138987799703, 0.2651100247519064, 0.25598662494655583, 0.24650742572135614, 0.23667803771374105, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.22650340375289493, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
-      "\n",
-      "\n",
-      "PermShkStd\n",
-      "current:  [0.2510928434853925, 0.2510928434853925, 0.2401556615138524, 0.2296573637153073, 0.21962767025150254, 0.21009896926797608, 0.20110619927478804, 0.19268653734486382, 0.18487882622158372, 0.1777226720373576, 0.17125715641406813, 0.16551913914067518, 0.16054118533450362, 0.15634923267877057, 0.15296020779851646, 0.15037988115495657, 0.1486012841616015, 0.14760397284848523, 0.14735430340072547, 0.14780671189307767, 0.14890581414586726, 0.15058901548290318, 0.15278927648771473, 0.15543771871404774, 0.15846584612550016, 0.16180726687440725, 0.16539889573485103, 0.16918168433697292, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
-      "original: [0.2510928434853925, 0.2510928434853925, 0.2401556615138524, 0.2296573637153073, 0.21962767025150254, 0.21009896926797608, 0.20110619927478804, 0.19268653734486382, 0.18487882622158372, 0.1777226720373576, 0.17125715641406813, 0.16551913914067518, 0.16054118533450362, 0.15634923267877057, 0.15296020779851646, 0.15037988115495657, 0.1486012841616015, 0.14760397284848523, 0.14735430340072547, 0.14780671189307767, 0.14890581414586726, 0.15058901548290318, 0.15278927648771473, 0.15543771871404774, 0.15846584612550016, 0.16180726687440725, 0.16539889573485103, 0.16918168433697292, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.17310096205313927, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
-      "\n",
-      "\n",
-      "P0\n",
-      "current:  21.397337769996003\n",
-      "original: 21.397337769996003\n",
-      "\n",
-      "\n",
-      "seed\n",
-      "current:  31382\n",
-      "original: 0\n",
-      "\n",
-      "\n",
-      "AgentCount\n",
-      "current:  10000\n",
-      "original: 10000\n",
-      "\n",
-      "\n",
-      "Rfree\n",
-      "current:  1.03\n",
-      "original: 1.03\n",
-      "\n",
-      "\n",
-      "tax_rate\n",
-      "current:  0.0\n",
-      "original: 0.0\n",
-      "\n",
-      "\n",
-      "aXtraCount\n",
-      "current:  200\n",
-      "original: 48\n",
-      "\n",
-      "\n",
-      "aXtraMin\n",
-      "current:  0.001\n",
-      "original: 0.001\n",
-      "\n",
-      "\n",
-      "vFuncBool\n",
-      "current:  False\n",
-      "original: False\n",
-      "\n",
-      "\n",
-      "IncShkDstn\n",
-      "current:  <HARK.distribution.IndexDistribution object at 0x7f79be35dde0>\n",
-      "original: <HARK.distribution.IndexDistribution object at 0x7f79be56fd00>\n",
-      "\n",
-      "\n",
-      "UnempPrbRet\n",
-      "current:  0.005\n",
-      "original: 0.005\n",
-      "\n",
-      "\n",
-      "state_prev\n",
-      "current:  {'pLvl': None, 'PlvlAgg': None, 'bNrm': None, 'mNrm': None, 'aNrm': None, 'aLvl': None}\n",
-      "original: {'pLvl': None, 'PlvlAgg': None, 'bNrm': None, 'mNrm': None, 'aNrm': None, 'aLvl': None}\n",
-      "\n",
-      "\n",
-      "PermShkCount\n",
-      "current:  7\n",
-      "original: 7\n",
-      "\n",
-      "\n",
-      "neutral_measure\n",
-      "current:  False\n",
-      "original: False\n",
-      "\n",
-      "\n",
-      "tolerance\n",
-      "current:  1e-06\n",
-      "original: 1e-06\n",
-      "\n",
-      "\n",
-      "time_vary\n",
-      "current:  ['LivPrb', 'PermGroFac', 'IncShkDstn', 'PermShkDstn', 'TranShkDstn', 'DiscFac']\n",
-      "original: ['LivPrb', 'PermGroFac', 'IncShkDstn', 'PermShkDstn', 'TranShkDstn', 'DiscFac']\n",
-      "\n",
-      "\n",
-      "aXtraMax\n",
-      "current:  100\n",
-      "original: 20\n",
-      "\n",
-      "\n",
-      "newborn_init_history\n",
-      "current:  {}\n",
-      "original: {}\n",
-      "\n",
-      "\n",
-      "time_inv\n",
-      "current:  ['CRRA', 'BoroCnstArt', 'BoroCnstArt', 'vFuncBool', 'CubicBool', 'Rfree', 'aXtraGrid']\n",
-      "original: ['CRRA', 'BoroCnstArt', 'BoroCnstArt', 'vFuncBool', 'CubicBool', 'Rfree', 'aXtraGrid']\n",
-      "\n",
-      "\n",
-      "PermShkDstn\n",
-      "current:  <HARK.distribution.IndexDistribution object at 0x7f79be35f910>\n",
-      "original: <HARK.distribution.IndexDistribution object at 0x7f79be3b9b10>\n",
-      "\n",
-      "\n",
-      "aXtraGrid\n",
-      "current:  [1.00000000e-03 2.44808926e-02 4.85125868e-02 7.31080029e-02\n",
-      " 9.82803643e-02 1.24043205e-01 1.50410375e-01 1.77396052e-01\n",
-      " 2.05014744e-01 2.33281299e-01 2.62210915e-01 2.91819146e-01\n",
-      " 3.22121910e-01 3.53135499e-01 3.84876587e-01 4.17362240e-01\n",
-      " 4.50609923e-01 4.84637511e-01 5.19463300e-01 5.55106012e-01\n",
-      " 5.91584810e-01 6.28919308e-01 6.67129577e-01 7.06236161e-01\n",
-      " 7.46260085e-01 7.87222868e-01 8.29146533e-01 8.72053619e-01\n",
-      " 9.15967195e-01 9.60910872e-01 1.00690881e+00 1.05398574e+00\n",
-      " 1.10216698e+00 1.15147843e+00 1.20194659e+00 1.25359861e+00\n",
-      " 1.30646226e+00 1.36056595e+00 1.41593877e+00 1.47261050e+00\n",
-      " 1.53061160e+00 1.58997325e+00 1.65072738e+00 1.71290665e+00\n",
-      " 1.77654448e+00 1.84167509e+00 1.90833350e+00 1.97655555e+00\n",
-      " 2.04637791e+00 2.11783812e+00 2.19097460e+00 2.26582668e+00\n",
-      " 2.34243460e+00 2.42083955e+00 2.50108367e+00 2.58321011e+00\n",
-      " 2.66726303e+00 2.75328762e+00 2.84133012e+00 2.93143787e+00\n",
-      " 3.02365932e+00 3.11804405e+00 3.21464280e+00 3.31350751e+00\n",
-      " 3.41469133e+00 3.51824867e+00 3.62423519e+00 3.73270789e+00\n",
-      " 3.84372508e+00 3.95734644e+00 4.07363308e+00 4.19264750e+00\n",
-      " 4.31445369e+00 4.43911714e+00 4.56670488e+00 4.69728550e+00\n",
-      " 4.83092920e+00 4.96770785e+00 5.10769497e+00 5.25096583e+00\n",
-      " 5.39759745e+00 5.54766868e+00 5.70126020e+00 5.85845457e+00\n",
-      " 6.01933633e+00 6.18399195e+00 6.35250998e+00 6.52498101e+00\n",
-      " 6.70149776e+00 6.88215514e+00 7.06705029e+00 7.25628260e+00\n",
-      " 7.44995381e+00 7.64816806e+00 7.85103190e+00 8.05865441e+00\n",
-      " 8.27114720e+00 8.48862454e+00 8.71120333e+00 8.93900326e+00\n",
-      " 9.17214678e+00 9.41075926e+00 9.65496897e+00 9.90490721e+00\n",
-      " 1.01607084e+01 1.04225100e+01 1.06904527e+01 1.09646808e+01\n",
-      " 1.12453415e+01 1.15325858e+01 1.18265681e+01 1.21274465e+01\n",
-      " 1.24353828e+01 1.27505424e+01 1.30730948e+01 1.34032135e+01\n",
-      " 1.37410760e+01 1.40868638e+01 1.44407630e+01 1.48029636e+01\n",
-      " 1.51736606e+01 1.55530532e+01 1.59413454e+01 1.63387459e+01\n",
-      " 1.67454684e+01 1.71617316e+01 1.75877593e+01 1.80237804e+01\n",
-      " 1.84700295e+01 1.89267465e+01 1.93941768e+01 1.98725719e+01\n",
-      " 2.03621889e+01 2.08632911e+01 2.13761478e+01 2.19010348e+01\n",
-      " 2.24382344e+01 2.29880353e+01 2.35507330e+01 2.41266303e+01\n",
-      " 2.47160366e+01 2.53192688e+01 2.59366514e+01 2.65685161e+01\n",
-      " 2.72152028e+01 2.78770591e+01 2.85544408e+01 2.92477122e+01\n",
-      " 2.99572460e+01 3.06834236e+01 3.14266354e+01 3.21872811e+01\n",
-      " 3.29657696e+01 3.37625195e+01 3.45779590e+01 3.54125267e+01\n",
-      " 3.62666712e+01 3.71408517e+01 3.80355382e+01 3.89512119e+01\n",
-      " 3.98883648e+01 4.08475010e+01 4.18291360e+01 4.28337977e+01\n",
-      " 4.38620262e+01 4.49143743e+01 4.59914077e+01 4.70937056e+01\n",
-      " 4.82218606e+01 4.93764792e+01 5.05581822e+01 5.17676049e+01\n",
-      " 5.30053976e+01 5.42722257e+01 5.55687704e+01 5.68957286e+01\n",
-      " 5.82538139e+01 5.96437564e+01 6.10663034e+01 6.25222197e+01\n",
-      " 6.40122881e+01 6.55373096e+01 6.70981042e+01 6.86955111e+01\n",
-      " 7.03303890e+01 7.20036170e+01 7.37160946e+01 7.54687426e+01\n",
-      " 7.72625032e+01 7.90983407e+01 8.09772424e+01 8.29002182e+01\n",
-      " 8.48683022e+01 8.68825523e+01 8.89440516e+01 9.10539083e+01\n",
-      " 9.32132569e+01 9.54232583e+01 9.76851006e+01 1.00000000e+02]\n",
-      "original: [1.00000000e-03 2.01713727e-02 4.04645973e-02 6.19689346e-02\n",
-      " 8.47826891e-02 1.09014323e-01 1.34783729e-01 1.62223697e-01\n",
-      " 1.91481594e-01 2.22721307e-01 2.56125489e-01 2.91898165e-01\n",
-      " 3.30267760e-01 3.71490637e-01 4.15855231e-01 4.63686907e-01\n",
-      " 5.15353678e-01 5.71272965e-01 6.31919613e-01 6.97835428e-01\n",
-      " 7.69640582e-01 8.48047286e-01 9.33876256e-01 1.02807664e+00\n",
-      " 1.13175022e+00 1.24618095e+00 1.37287121e+00 1.51358644e+00\n",
-      " 1.67041051e+00 1.84581461e+00 2.04274370e+00 2.26472534e+00\n",
-      " 2.51600777e+00 2.80173618e+00 3.12817922e+00 3.50302228e+00\n",
-      " 3.93574988e+00 4.43814835e+00 5.02497206e+00 5.71483401e+00\n",
-      " 6.53140746e+00 7.50506263e+00 8.67511887e+00 1.00929770e+01\n",
-      " 1.18265253e+01 1.39664114e+01 1.66350835e+01 2.00000000e+01]\n",
-      "\n",
-      "\n",
-      "aNrmInitMean\n",
-      "current:  -0.928110551297082\n",
-      "original: -0.928110551297082\n",
-      "\n",
-      "\n",
-      "MaxKinks\n",
-      "current:  400\n",
-      "original: 400\n",
-      "\n",
-      "\n",
-      "solve_one_period\n",
-      "current:  <function make_one_period_oo_solver.<locals>.one_period_solver at 0x7f79be31a3b0>\n",
-      "original: <function make_one_period_oo_solver.<locals>.one_period_solver at 0x7f79be3d0550>\n",
-      "\n",
-      "\n",
-      "PermGroFacAgg\n",
-      "current:  1.0\n",
-      "original: 1.0\n",
-      "\n",
-      "\n",
-      "T_cycle\n",
-      "current:  65\n",
-      "original: 65\n",
-      "\n",
-      "\n",
-      "CRRA\n",
-      "current:  1.3759978446748666\n",
-      "original: 2.0\n",
-      "\n",
-      "\n",
-      "BoroCnstArt\n",
-      "current:  0.0\n",
-      "original: 0.0\n",
-      "\n",
-      "\n",
-      "TranShkDstn\n",
-      "current:  <HARK.distribution.IndexDistribution object at 0x7f7abc5a3a90>\n",
-      "original: <HARK.distribution.IndexDistribution object at 0x7f79be3bbc10>\n",
-      "\n",
-      "\n",
-      "read_shocks\n",
-      "current:  False\n",
-      "original: False\n",
-      "\n",
-      "\n",
-      "cycles\n",
-      "current:  1\n",
-      "original: 1\n",
-      "\n",
-      "\n",
-      "shock_vars\n",
-      "current:  ['PermShk', 'TranShk']\n",
-      "original: ['PermShk', 'TranShk']\n",
-      "\n",
-      "\n",
-      "aNrmInitStd\n",
-      "current:  1.6577133299830675\n",
-      "original: 1.6577133299830675\n",
-      "\n",
-      "\n",
-      "UnempPrb\n",
-      "current:  0.05\n",
-      "original: 0.05\n",
-      "\n",
-      "\n",
-      "RNG\n",
-      "current:  Generator(PCG64)\n",
-      "original: Generator(PCG64)\n",
-      "\n",
-      "\n",
-      "aXtraExtra\n",
-      "current:  [None, None]\n",
-      "original: [None]\n",
-      "\n",
-      "\n",
-      "pLvlInitMean\n",
-      "current:  3.0632665110178623\n",
-      "original: 3.0632665110178623\n",
-      "\n",
-      "\n",
-      "DiscFac\n",
-      "current:  [0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122, 0.9552205116274122]\n",
-      "original: [0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96]\n",
-      "\n",
-      "\n",
-      "pseudo_terminal\n",
-      "current:  False\n",
-      "original: False\n",
-      "\n",
-      "\n",
-      "pLvlInitStd\n",
-      "current:  0.758541123818503\n",
-      "original: 0.758541123818503\n",
-      "\n",
-      "\n",
-      "T_retire\n",
-      "current:  39\n",
-      "original: 0\n",
-      "\n",
-      "\n",
-      "verbose\n",
-      "current:  1\n",
-      "original: 1\n",
-      "\n",
-      "\n",
-      "history\n",
-      "current:  {}\n",
-      "original: {}\n",
-      "\n",
-      "\n",
-      "LivPrb\n",
-      "current:  [0.998341, 0.998262, 0.99826, 0.998172, 0.99803, 0.99796, 0.997886, 0.997792, 0.997587, 0.99747, 0.997398, 0.997621, 0.997822, 0.997755, 0.997607, 0.997421, 0.99722, 0.996942, 0.996701, 0.996562, 0.996243, 0.996023, 0.995789, 0.995449, 0.995122, 0.994844, 0.994377, 0.993913, 0.993402, 0.992824, 0.992191, 0.991511, 0.990844, 0.990081, 0.989317, 0.988495, 0.987654, 0.986892, 0.986244, 0.985647, 0.984987, 0.984198, 0.983305, 0.982293, 0.981146, 0.979812, 0.97829, 0.976614, 0.974779, 0.972732, 0.970243, 0.967372, 0.964395, 0.9614, 0.958184, 0.954529, 0.950045, 0.944392, 0.937261, 0.928746, 0.9191320000000001, 0.908706, 0.897671, 0.886107, 0.873964]\n",
-      "original: [0.998566, 0.998583, 0.998599, 0.998609, 0.998611, 0.99861, 0.998601, 0.998569, 0.998508, 0.998419, 0.998312, 0.998192, 0.998056, 0.997906, 0.99774, 0.997556, 0.997348, 0.997115, 0.996852, 0.996562, 0.996249, 0.995916, 0.995561, 0.995186, 0.99479, 0.994349, 0.993881, 0.993428, 0.993005, 0.992583, 0.992124, 0.991583, 0.990942, 0.990175, 0.98929, 0.988296, 0.987216, 0.986059, 0.984831, 0.983509, 0.982022, 0.980368, 0.978602, 0.976732, 0.974708, 0.97243, 0.969863, 0.967036, 0.963933, 0.960506, 0.956589, 0.952211, 0.947534, 0.942585, 0.937209, 0.931163, 0.924276, 0.916534, 0.907855, 0.898197, 0.887532, 0.8758360000000001, 0.863084, 0.849246, 0.834296]\n",
-      "\n",
-      "\n",
-      "solution_terminal\n",
-      "current:  <HARK.ConsumptionSaving.ConsIndShockModel.ConsumerSolution object at 0x7f79be35ff70>\n",
-      "original: <HARK.ConsumptionSaving.ConsIndShockModel.ConsumerSolution object at 0x7f79be56fcd0>\n",
-      "\n",
-      "\n",
-      "IncUnempRet\n",
-      "current:  0.0\n",
-      "original: 0.0\n",
-      "\n",
-      "\n",
-      "NewbornTransShk\n",
-      "current:  False\n",
-      "original: False\n",
-      "\n",
-      "\n",
-      "shock_history\n",
-      "current:  {}\n",
-      "original: {}\n",
-      "\n",
-      "\n",
-      "CubicBool\n",
-      "current:  False\n",
-      "original: False\n",
-      "\n",
-      "\n",
-      "TranShkCount\n",
-      "current:  7\n",
-      "original: 7\n",
-      "\n",
-      "\n",
-      "IncUnemp\n",
-      "current:  0.3\n",
-      "original: 0.3\n",
-      "\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "param_list = set(lifecycle_agent.__dict__.keys()).union(\n",
-    "    set(indshk_agent.__dict__.keys())\n",
-    ")\n",
-    "\n",
-    "for param in param_list:\n",
-    "    current_value = getattr(indshk_agent, param, None)\n",
-    "    original_value = getattr(lifecycle_agent, param, None)\n",
-    "\n",
-    "    if current_value is None:\n",
-    "        print(f\"{param} not in indshk_agent\\n\")\n",
-    "    elif original_value is None:\n",
-    "        print(f\"{param} not in lifecycle_agent\\n\")\n",
-    "    else:\n",
-    "        print(f\"{param}\")\n",
-    "        print(f\"current:  {current_value}\")\n",
-    "        print(f\"original: {original_value}\")\n",
-    "        print(\"\\n\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "lifecycle_agent.solve()\n",
-    "# Set up the variables we want to keep track of.\n",
-    "lifecycle_agent.track_vars = [\"aNrm\", \"cNrm\", \"pLvl\", \"t_age\", \"mNrm\"]\n",
-    "\n",
-    "lifecycle_agent.T_sim = 200\n",
-    "# Run the simulations\n",
-    "lifecycle_agent.initialize_sim()\n",
-    "history = lifecycle_agent.simulate()\n",
-    "\n",
-    "raw_data = {\n",
-    "    \"Age\": lifecycle_agent.history[\"t_age\"].flatten() + 25 - 1,\n",
-    "    \"pIncome\": lifecycle_agent.history[\"pLvl\"].flatten(),\n",
-    "    \"nrmM\": lifecycle_agent.history[\"mNrm\"].flatten(),\n",
-    "    \"nrmC\": lifecycle_agent.history[\"cNrm\"].flatten(),\n",
-    "}\n",
-    "\n",
-    "Data = pd.DataFrame(raw_data)\n",
-    "Data[\"Cons\"] = Data.nrmC * Data.pIncome\n",
-    "Data[\"M\"] = Data.nrmM * Data.pIncome\n",
-    "\n",
-    "# Find the mean of each variable at every age\n",
-    "AgeMeans = Data.groupby([\"Age\"]).median().reset_index()\n",
-    "\n",
-    "plt.figure()\n",
-    "plt.plot(AgeMeans.Age, AgeMeans.pIncome, label=\"Permanent Income\")\n",
-    "plt.plot(AgeMeans.Age, AgeMeans.M, label=\"Market resources\")\n",
-    "plt.plot(AgeMeans.Age, AgeMeans.Cons, label=\"Consumption\")\n",
-    "plt.legend()\n",
-    "plt.xlabel(\"Age\")\n",
-    "plt.ylabel(\"Thousands of USD\")\n",
-    "plt.title(\"Variable Medians Conditional on Survival\")\n",
-    "plt.grid()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.13"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
+    "nbformat": 4,
+    "nbformat_minor": 4
+}
\ No newline at end of file
diff --git a/code/Portfolio.ipynb b/code/Portfolio.ipynb
index 93f3f40..60269d7 100644
--- a/code/Portfolio.ipynb
+++ b/code/Portfolio.ipynb
@@ -4,18 +4,10 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "***NOTE: using a 'quick fix' for an attribute error. See 'Error Notes' in EstimationParameter.py for further discussion.***\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from estimark.agents import PortfolioLifeCycleConsumerType\n",
-    "import estimark.calibration.estimation_parameters as parameters\n",
+    "import estimark.calibration.parameters as parameters\n",
     "import numpy as np\n",
     "from HARK.utilities import plot_funcs\n",
     "import matplotlib.pyplot as plt\n",
@@ -29,7 +21,9 @@
    "outputs": [],
    "source": [
     "DiscFacAdj, CRRA = np.genfromtxt(\n",
-    "    \"tables/Portfolio_estimate_results.csv\", skip_header=1, delimiter=\",\"\n",
+    "    \"tables/PortfolioSub(Stock)(Labor)Market_estimate_results.csv\",\n",
+    "    skip_header=1,\n",
+    "    delimiter=\",\",\n",
     ")"
    ]
   },
@@ -37,7 +31,19 @@
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "module 'estimark.calibration.parameters' has no attribute 'DiscFac_timevary'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[3], line 7\u001b[0m\n\u001b[0;32m      1\u001b[0m portfolio_agent \u001b[38;5;241m=\u001b[39m PortfolioLifeCycleConsumerType(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mparameters\u001b[38;5;241m.\u001b[39minit_consumer_objects)\n\u001b[0;32m      4\u001b[0m portfolio_agent\u001b[38;5;241m.\u001b[39mCRRA \u001b[38;5;241m=\u001b[39m CRRA\n\u001b[1;32m----> 7\u001b[0m portfolio_agent\u001b[38;5;241m.\u001b[39mDiscFac \u001b[38;5;241m=\u001b[39m [b \u001b[38;5;241m*\u001b[39m DiscFacAdj \u001b[38;5;28;01mfor\u001b[39;00m b \u001b[38;5;129;01min\u001b[39;00m \u001b[43mparameters\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mDiscFac_timevary\u001b[49m]\n",
+      "\u001b[1;31mAttributeError\u001b[0m: module 'estimark.calibration.parameters' has no attribute 'DiscFac_timevary'"
+     ]
+    }
+   ],
    "source": [
     "portfolio_agent = PortfolioLifeCycleConsumerType(**parameters.init_consumer_objects)\n",
     "\n",
@@ -45,12 +51,12 @@
     "portfolio_agent.CRRA = CRRA\n",
     "\n",
     "\n",
-    "portfolio_agent.DiscFac = [b * DiscFacAdj for b in parameters.DiscFac_timevary]"
+    "portfolio_agent.DiscFac = [b * DiscFacAdj for b in parameters.timevary_DiscFac]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -59,7 +65,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -89,7 +95,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -119,7 +125,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -134,7 +140,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -153,7 +159,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -185,7 +191,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
diff --git a/code/WarmGlow.ipynb b/code/WarmGlow.ipynb
index a8d8019..16974a5 100644
--- a/code/WarmGlow.ipynb
+++ b/code/WarmGlow.ipynb
@@ -1,173 +1,173 @@
 {
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "***NOTE: using a 'quick fix' for an attribute error. See 'Error Notes' in EstimationParameter.py for further discussion.***\n"
-     ]
-    }
-   ],
-   "source": [
-    "from estimark.agents import BequestWarmGlowLifeCycleConsumerType\n",
-    "import estimark.calibration.estimation_parameters as parameters\n",
-    "import numpy as np\n",
-    "from HARK.utilities import plot_funcs\n",
-    "import matplotlib.pyplot as plt\n",
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "DiscFacAdj, CRRA = np.genfromtxt(\n",
-    "    \"tables/WarmGlow_estimate_results.csv\", skip_header=1, delimiter=\",\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "indshk_agent = BequestWarmGlowLifeCycleConsumerType(**parameters.init_consumer_objects)\n",
-    "\n",
-    "indshk_agent.CRRA = CRRA\n",
-    "indshk_agent.DiscFac = [b * DiscFacAdj for b in parameters.DiscFac_timevary]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "indshk_agent.solve()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+    "cells": [
+        {
+            "cell_type": "code",
+            "execution_count": 1,
+            "metadata": {},
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "***NOTE: using a 'quick fix' for an attribute error. See 'Error Notes' in EstimationParameter.py for further discussion.***\n"
+                    ]
+                }
+            ],
+            "source": [
+                "from estimark.agents import BequestWarmGlowLifeCycleConsumerType\n",
+                "import estimark.calibration.parameters as parameters\n",
+                "import numpy as np\n",
+                "from HARK.utilities import plot_funcs\n",
+                "import matplotlib.pyplot as plt\n",
+                "import pandas as pd"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 2,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "DiscFacAdj, CRRA = np.genfromtxt(\n",
+                "    \"tables/WarmGlow_estimate_results.csv\", skip_header=1, delimiter=\",\"\n",
+                ")"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 3,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "indshk_agent = BequestWarmGlowLifeCycleConsumerType(**parameters.init_consumer_objects)\n",
+                "\n",
+                "indshk_agent.CRRA = CRRA\n",
+                "indshk_agent.DiscFac = [b * DiscFacAdj for b in parameters.timevary_DiscFac]"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 4,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "indshk_agent.solve()"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 5,
+            "metadata": {},
+            "outputs": [
+                {
+                    "data": {
+                        "image/png": 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",
+                        "text/plain": [
+                            "<Figure size 640x480 with 1 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "data": {
+                        "text/plain": [
+                            "<Figure size 640x480 with 0 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                }
+            ],
+            "source": [
+                "plot_funcs([sol.cFunc for sol in indshk_agent.solution[:-1:5]], 0, 20)\n",
+                "plt.savefig(\"../content/figures/WarmGlowIndShock_cFunc.png\")"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 6,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "# Set up the variables we want to keep track of.\n",
+                "indshk_agent.track_vars = [\"aNrm\", \"cNrm\", \"pLvl\", \"t_age\", \"mNrm\"]\n",
+                "\n",
+                "indshk_agent.T_sim = 200\n",
+                "# Run the simulations\n",
+                "indshk_agent.initialize_sim()\n",
+                "history = indshk_agent.simulate()"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 7,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "raw_data = {\n",
+                "    \"Age\": indshk_agent.history[\"t_age\"].flatten() + 25 - 1,\n",
+                "    \"pIncome\": indshk_agent.history[\"pLvl\"].flatten(),\n",
+                "    \"nrmM\": indshk_agent.history[\"mNrm\"].flatten(),\n",
+                "    \"nrmC\": indshk_agent.history[\"cNrm\"].flatten(),\n",
+                "}\n",
+                "\n",
+                "Data = pd.DataFrame(raw_data)\n",
+                "Data[\"Cons\"] = Data.nrmC * Data.pIncome\n",
+                "Data[\"M\"] = Data.nrmM * Data.pIncome"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 8,
+            "metadata": {},
+            "outputs": [
+                {
+                    "data": {
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",
+                        "text/plain": [
+                            "<Figure size 640x480 with 1 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                }
+            ],
+            "source": [
+                "# Find the mean of each variable at every age\n",
+                "AgeMeans = Data.groupby([\"Age\"]).median().reset_index()\n",
+                "\n",
+                "plt.figure()\n",
+                "plt.plot(AgeMeans.Age, AgeMeans.pIncome, label=\"Permanent Income\")\n",
+                "plt.plot(AgeMeans.Age, AgeMeans.M, label=\"Market resources\")\n",
+                "plt.plot(AgeMeans.Age, AgeMeans.Cons, label=\"Consumption\")\n",
+                "plt.legend()\n",
+                "plt.xlabel(\"Age\")\n",
+                "plt.ylabel(\"Thousands of USD\")\n",
+                "plt.title(\"Variable Medians Conditional on Survival\")\n",
+                "plt.grid()"
+            ]
+        }
+    ],
+    "metadata": {
+        "kernelspec": {
+            "display_name": "Python 3 (ipykernel)",
+            "language": "python",
+            "name": "python3"
+        },
+        "language_info": {
+            "codemirror_mode": {
+                "name": "ipython",
+                "version": 3
+            },
+            "file_extension": ".py",
+            "mimetype": "text/x-python",
+            "name": "python",
+            "nbconvert_exporter": "python",
+            "pygments_lexer": "ipython3",
+            "version": "3.10.13"
+        }
     },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 640x480 with 0 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_funcs([sol.cFunc for sol in indshk_agent.solution[:-1:5]], 0, 20)\n",
-    "plt.savefig(\"../content/figures/WarmGlowIndShock_cFunc.png\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Set up the variables we want to keep track of.\n",
-    "indshk_agent.track_vars = [\"aNrm\", \"cNrm\", \"pLvl\", \"t_age\", \"mNrm\"]\n",
-    "\n",
-    "indshk_agent.T_sim = 200\n",
-    "# Run the simulations\n",
-    "indshk_agent.initialize_sim()\n",
-    "history = indshk_agent.simulate()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "raw_data = {\n",
-    "    \"Age\": indshk_agent.history[\"t_age\"].flatten() + 25 - 1,\n",
-    "    \"pIncome\": indshk_agent.history[\"pLvl\"].flatten(),\n",
-    "    \"nrmM\": indshk_agent.history[\"mNrm\"].flatten(),\n",
-    "    \"nrmC\": indshk_agent.history[\"cNrm\"].flatten(),\n",
-    "}\n",
-    "\n",
-    "Data = pd.DataFrame(raw_data)\n",
-    "Data[\"Cons\"] = Data.nrmC * Data.pIncome\n",
-    "Data[\"M\"] = Data.nrmM * Data.pIncome"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Find the mean of each variable at every age\n",
-    "AgeMeans = Data.groupby([\"Age\"]).median().reset_index()\n",
-    "\n",
-    "plt.figure()\n",
-    "plt.plot(AgeMeans.Age, AgeMeans.pIncome, label=\"Permanent Income\")\n",
-    "plt.plot(AgeMeans.Age, AgeMeans.M, label=\"Market resources\")\n",
-    "plt.plot(AgeMeans.Age, AgeMeans.Cons, label=\"Consumption\")\n",
-    "plt.legend()\n",
-    "plt.xlabel(\"Age\")\n",
-    "plt.ylabel(\"Thousands of USD\")\n",
-    "plt.title(\"Variable Medians Conditional on Survival\")\n",
-    "plt.grid()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.13"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
+    "nbformat": 4,
+    "nbformat_minor": 4
+}
\ No newline at end of file
diff --git a/code/WarmGlowPortfolio.ipynb b/code/WarmGlowPortfolio.ipynb
index b4932cc..d38b5b0 100644
--- a/code/WarmGlowPortfolio.ipynb
+++ b/code/WarmGlowPortfolio.ipynb
@@ -15,7 +15,7 @@
             ],
             "source": [
                 "from estimark.agents import BequestWarmGlowLifeCyclePortfolioType\n",
-                "import estimark.calibration.estimation_parameters as parameters\n",
+                "import estimark.calibration.parameters as parameters\n",
                 "import numpy as np\n",
                 "from HARK.utilities import plot_funcs\n",
                 "import matplotlib.pyplot as plt"
@@ -43,7 +43,7 @@
                 ")\n",
                 "\n",
                 "portfolio_agent.CRRA = CRRA\n",
-                "portfolio_agent.DiscFac = [b * DiscFacAdj for b in parameters.DiscFac_timevary]"
+                "portfolio_agent.DiscFac = [b * DiscFacAdj for b in parameters.timevary_DiscFac]"
             ]
         },
         {
@@ -164,4 +164,4 @@
     },
     "nbformat": 4,
     "nbformat_minor": 4
-}
+}
\ No newline at end of file
diff --git a/code/WealthPortfolio.ipynb b/code/WealthPortfolio.ipynb
index cde5be4..3b9ac65 100644
--- a/code/WealthPortfolio.ipynb
+++ b/code/WealthPortfolio.ipynb
@@ -15,7 +15,7 @@
             ],
             "source": [
                 "from estimark.agents import WealthPortfolioLifeCycleConsumerType\n",
-                "import estimark.calibration.estimation_parameters as parameters\n",
+                "import estimark.calibration.parameters as parameters\n",
                 "import numpy as np\n",
                 "from HARK.utilities import plot_funcs\n",
                 "import matplotlib.pyplot as plt\n",
@@ -44,7 +44,7 @@
                 ")\n",
                 "\n",
                 "portfolio_agent.CRRA = CRRA\n",
-                "portfolio_agent.DiscFac = [b * DiscFacAdj for b in parameters.DiscFac_timevary]"
+                "portfolio_agent.DiscFac = [b * DiscFacAdj for b in parameters.timevary_DiscFac]"
             ]
         },
         {
@@ -265,4 +265,4 @@
     },
     "nbformat": 4,
     "nbformat_minor": 4
-}
+}
\ No newline at end of file
diff --git a/code/estimark/estimation.py b/code/estimark/estimation.py
index 4f6189a..e9fbfd9 100644
--- a/code/estimark/estimation.py
+++ b/code/estimark/estimation.py
@@ -20,6 +20,7 @@
 from HARK.distribution import DiscreteDistribution
 
 # Estimation methods
+# todo: use estimagic
 from HARK.estimation import bootstrap_sample_from_data, minimize_nelder_mead
 from scipy.optimize import approx_fprime
 
@@ -63,7 +64,7 @@
 local_agent_name = "IndShock"
 local_estimate_model = True  # Whether to estimate the model
 # Whether to get standard errors via bootstrap
-local_compute_standard_errors = False
+local_compute_se_bootstrap = False
 # Whether to compute a measure of estimates' sensitivity to moments
 local_compute_sensitivity = True
 # Whether to make a contour map of the objective function
@@ -136,11 +137,9 @@ def weighted_median(values, weights):
     return median
 
 
-def get_target_moments(
-    data=scf_data, weights=scf_weights, groups=scf_groups, mapping=scf_mapping
-):
+def get_targeted_moments(data=scf_data, weights=scf_weights, groups=scf_groups):
     # Initialize
-    group_count = len(mapping)
+    group_count = len(scf_mapping)
     target_moments = np.zeros(group_count)
 
     for g in range(group_count):
@@ -170,26 +169,24 @@ def get_initial_guess(agent_name):
 
 
 # Define the objective function for the simulated method of moments estimation
-def simulate_moments(
-    DiscFacAdj,
-    CRRA,
-    agent,
-    bounds_DiscFacAdj=options["bounds_DiscFacAdj"],
-    bounds_CRRA=options["bounds_CRRA"],
-    mapping=scf_mapping,
-):
+# todo: params, bounds, agent
+def simulate_moments(DiscFacAdj, CRRA, agent):
     """
     A quick check to make sure that the parameter values are within bounds.
     Far flung falues of DiscFacAdj or CRRA might cause an error during solution or
     simulation, so the objective function doesn't even bother with them.
     """
+
+    bounds_DiscFacAdj = options["bounds_DiscFacAdj"]
+    bounds_CRRA = options["bounds_CRRA"]
+
     if (
         DiscFacAdj < bounds_DiscFacAdj[0]
         or DiscFacAdj > bounds_DiscFacAdj[1]
         or CRRA < bounds_CRRA[0]
         or CRRA > bounds_CRRA[1]
     ):
-        return 1e30 * np.ones(len(mapping))
+        return 1e30 * np.ones(len(scf_mapping))
 
     # Update the agent with a new path of DiscFac based on this DiscFacAdj (and a new CRRA)
     agent.DiscFac = [b * DiscFacAdj for b in options["timevary_DiscFac"]]
@@ -198,7 +195,7 @@ def simulate_moments(
     agent.solve()  # Solve the microeconomic model
     # "Unpack" the consumption function for convenient access
     # agent.unpack("cFunc")
-    max_sim_age = max([max(ages) for ages in mapping]) + 1
+    max_sim_age = max([max(ages) for ages in scf_mapping]) + 1
     # Initialize the simulation by clearing histories, resetting initial values
     agent.initialize_sim()
     agent.simulate(max_sim_age)  # Simulate histories of consumption and wealth
@@ -206,11 +203,11 @@ def simulate_moments(
     sim_w_history = agent.history["bNrm"]
 
     # Find the distance between empirical data and simulated medians for each age group
-    group_count = len(mapping)
+    group_count = len(scf_mapping)
     sim_moments = []
     for g in range(group_count):
         # The simulated time indices corresponding to this age group
-        cohort_indices = mapping[g]
+        cohort_indices = scf_mapping[g]
         # The median of simulated wealth-to-income for this age group
         sim_moments += [np.median(sim_w_history[cohort_indices])]
 
@@ -219,15 +216,7 @@ def simulate_moments(
     return sim_moments
 
 
-def smm_obj_func(
-    DiscFacAdj,
-    CRRA,
-    agent,
-    target_moments,
-    bounds_DiscFacAdj=options["bounds_DiscFacAdj"],
-    bounds_CRRA=options["bounds_CRRA"],
-    mapping=scf_mapping,
-):
+def smm_obj_func(DiscFacAdj, CRRA, agent, moments):
     """
     The objective function for the SMM estimation.  Given values of discount factor
     adjuster DiscFacAdj, coeffecient of relative risk aversion CRRA, a base consumer
@@ -275,22 +264,15 @@ def smm_obj_func(
         median wealth-to-permanent-income ratio in the simulation.
     """
 
-    sim_moments = simulate_moments(
-        DiscFacAdj,
-        CRRA,
-        agent,
-        bounds_DiscFacAdj,
-        bounds_CRRA,
-        mapping,
-    )
-    errors = target_moments - sim_moments
+    sim_moments = simulate_moments(DiscFacAdj, CRRA, agent)
+    errors = moments - sim_moments
     loss = np.dot(errors, errors)
 
     return loss
 
 
 # Define the bootstrap procedure
-def calculate_std_err_bootstrap(initial_estimate, N, agent, seed=0, verbose=False):
+def calculate_se_bootstrap(initial_estimate, N, agent, seed=0, verbose=False):
     """
     Calculates standard errors by repeatedly re-estimating the model with datasets
     resampled from the actual data.
@@ -325,26 +307,22 @@ def calculate_std_err_bootstrap(initial_estimate, N, agent, seed=0, verbose=Fals
 
         # Bootstrap a new dataset by resampling from the original data
         bootstrap_data = (bootstrap_sample_from_data(scf_array, seed=seed_list[n])).T
-        w_to_y_data_bootstrap = bootstrap_data[0]
-        empirical_groups_bootstrap = bootstrap_data[1]
-        empirical_weights_bootstrap = bootstrap_data[2]
+        data_bootstrap = bootstrap_data[0]
+        groups_bootstrap = bootstrap_data[1]
+        weights_bootstrap = bootstrap_data[2]
 
         # Find moments with bootstrapped sample
-        bstrap_target_moments = get_target_moments(
-            data=w_to_y_data_bootstrap,
-            weights=empirical_weights_bootstrap,
-            groups=empirical_groups_bootstrap,
-            mapping=scf_mapping,
+        bootstrap_moments = get_targeted_moments(
+            data=data_bootstrap, weights=weights_bootstrap, groups=groups_bootstrap
         )
 
         # Make a temporary function for use in this estimation run
-        def smm_obj_func_bootstrap(parameters_to_estimate):
+        def smm_obj_func_bootstrap(params):
             return smm_obj_func(
-                DiscFacAdj=parameters_to_estimate[0],
-                CRRA=parameters_to_estimate[1],
+                DiscFacAdj=params[0],
+                CRRA=params[1],
                 agent=agent,
-                target_moments=bstrap_target_moments,
-                mapping=scf_mapping,
+                moments=bootstrap_moments,
             )
 
         # Estimate the model with the bootstrap data and add to list of estimates
@@ -371,7 +349,7 @@ def smm_obj_func_bootstrap(parameters_to_estimate):
 # =================================================================
 
 
-def estimate_model_opt(agent_name, estimation_agent, target_moments, initial_guess):
+def do_estimate_model(agent_name, estimation_agent, target_moments, initial_guess):
     print("----------------------------------------------------------------------")
     print(
         f"Now estimating the model using Nelder-Mead from an initial guess of {initial_guess}..."
@@ -379,17 +357,17 @@ def estimate_model_opt(agent_name, estimation_agent, target_moments, initial_gue
     print("----------------------------------------------------------------------")
 
     # Make a single-input lambda function for use in the optimizer
-    def smm_obj_func_redux(parameters_to_estimate):
+    def smm_obj_func_redux(params):
         """
         A "reduced form" of the SMM objective function, compatible with the optimizer.
         Identical to smmObjectiveFunction, but takes only a single input as a length-2
         list representing [DiscFacAdj,CRRA].
         """
         return smm_obj_func(
-            DiscFacAdj=parameters_to_estimate[0],
-            CRRA=parameters_to_estimate[1],
+            DiscFacAdj=params[0],
+            CRRA=params[1],
             agent=estimation_agent,
-            target_moments=target_moments,
+            moments=target_moments,
         )
 
     t_start_estimate = time()
@@ -417,7 +395,7 @@ def smm_obj_func_redux(parameters_to_estimate):
     return model_estimate, time_to_estimate
 
 
-def compute_std_err_bootstrap(
+def do_compute_se_boostrap(
     agent_name, estimation_agent, model_estimate, time_to_estimate
 ):
     # Estimate the model:
@@ -432,7 +410,7 @@ def compute_std_err_bootstrap(
     print(f"This will take approximately {int(minutes)} min, {int(seconds)} sec.")
 
     t_start_bootstrap = time()
-    std_errors = calculate_std_err_bootstrap(
+    std_errors = calculate_se_bootstrap(
         model_estimate,
         N=options["bootstrap_size"],
         agent=estimation_agent,
@@ -466,23 +444,14 @@ def compute_std_err_bootstrap(
         )
 
 
-def compute_sensitivity_measure(
-    agent_name, estimation_agent, model_estimate, initial_guess
-):
+def do_compute_sensitivity(agent_name, estimation_agent, model_estimate, initial_guess):
     print("``````````````````````````````````````````````````````````````````````")
     print("Computing sensitivity measure.")
     print("``````````````````````````````````````````````````````````````````````")
 
     # Find the Jacobian of the function that simulates moments
-    def simulate_moments_redux(x):
-        moments = simulate_moments(
-            x[0],
-            x[1],
-            agent=estimation_agent,
-            bounds_DiscFacAdj=options["bounds_DiscFacAdj"],
-            bounds_CRRA=options["bounds_CRRA"],
-            mapping=scf_mapping,
-        )
+    def simulate_moments_redux(params):
+        moments = simulate_moments(params[0], params[1], agent=estimation_agent)
 
         return moments
 
@@ -491,7 +460,7 @@ def simulate_moments_redux(x):
         [
             approx_fprime(
                 model_estimate,
-                lambda x: simulate_moments_redux(x)[j],
+                lambda params: simulate_moments_redux(params)[j],
                 epsilon=0.01,
             )
             for j in range(n_moments)
@@ -525,9 +494,7 @@ def simulate_moments_redux(x):
     plt.show()
 
 
-def make_contour_plot_obj_func(
-    agent_name, estimation_agent, model_estimate, target_moments
-):
+def do_make_contour_plot(agent_name, estimation_agent, model_estimate, target_moments):
     print("``````````````````````````````````````````````````````````````````````")
     print("Creating the contour plot.")
     print("``````````````````````````````````````````````````````````````````````")
@@ -549,7 +516,7 @@ def make_contour_plot_obj_func(
                 DiscFacAdj,
                 CRRA,
                 agent=estimation_agent,
-                target_moments=target_moments,
+                moments=target_moments,
             )
     smm_contour = plt.contourf(CRRA_mesh, DiscFacAdj_mesh, smm_obj_levels, level_count)
     t_end_contour = time()
@@ -572,7 +539,7 @@ def make_contour_plot_obj_func(
 def estimate(
     agent_name=local_agent_name,
     estimate_model=local_estimate_model,
-    compute_standard_errors=local_compute_standard_errors,
+    compute_se_bootstrap=local_compute_se_bootstrap,
     compute_sensitivity=local_compute_sensitivity,
     make_contour_plot=local_make_contour_plot,
     subjective_stock=local_subjective_stock,
@@ -603,31 +570,31 @@ def estimate(
         subjective_labor=subjective_labor,
     )
 
-    target_moments = get_target_moments()
+    target_moments = get_targeted_moments()
 
     initial_guess = get_initial_guess(agent_name)
 
     # Estimate the model using Nelder-Mead
     if estimate_model:
-        model_estimate, time_to_estimate = estimate_model_opt(
+        model_estimate, time_to_estimate = do_estimate_model(
             agent_name, estimation_agent, target_moments, initial_guess
         )
 
         # Compute standard errors by bootstrap
-        if compute_standard_errors:
-            compute_std_err_bootstrap(
+        if compute_se_bootstrap:
+            do_compute_se_boostrap(
                 agent_name, estimation_agent, model_estimate, time_to_estimate
             )
 
         # Compute sensitivity measure
         if compute_sensitivity:
-            compute_sensitivity_measure(
+            do_compute_sensitivity(
                 agent_name, estimation_agent, model_estimate, initial_guess
             )
 
         # Make a contour plot of the objective function
         if make_contour_plot:
-            make_contour_plot_obj_func(
+            do_make_contour_plot(
                 agent_name, estimation_agent, model_estimate, target_moments
             )
 
diff --git a/code/estimark/options.py b/code/estimark/options.py
index 32051bb..e685be6 100644
--- a/code/estimark/options.py
+++ b/code/estimark/options.py
@@ -4,7 +4,7 @@
 low_resource = {
     "estimate_model": True,
     "make_contour_plot": False,
-    "compute_standard_errors": False,
+    "compute_se_bootstrap": False,
     "compute_sensitivity": False,
     "subjective_stock": False,
     "subjective_labor": False,
@@ -16,7 +16,7 @@
 medium_resource = {
     "estimate_model": True,
     "make_contour_plot": True,
-    "compute_standard_errors": False,
+    "compute_se_bootstrap": False,
     "compute_sensitivity": True,
     "subjective_stock": False,
     "subjective_labor": False,
@@ -28,7 +28,7 @@
 high_resource = {
     "estimate_model": True,
     "make_contour_plot": False,
-    "compute_standard_errors": True,
+    "compute_se_bootstrap": True,
     "compute_sensitivity": True,
     "subjective_stock": False,
     "subjective_labor": False,
@@ -40,7 +40,7 @@
 all_replications = {
     "estimate_model": True,
     "make_contour_plot": True,
-    "compute_standard_errors": True,
+    "compute_se_bootstrap": True,
     "compute_sensitivity": True,
     "subjective_stock": False,
     "subjective_labor": False,