diff --git a/notebooks/Chinese-Growth.ipynb b/notebooks/Chinese-Growth.ipynb index fc6b9e84..ce351fb5 100644 --- a/notebooks/Chinese-Growth.ipynb +++ b/notebooks/Chinese-Growth.ipynb @@ -237,7 +237,7 @@ "\n", "bottomDiscFac = 0.9800\n", "topDiscFac = 0.9934 \n", - "DiscFac_list = approxUniform(N=num_consumer_types,bot=bottomDiscFac,top=topDiscFac)[1]\n", + "DiscFac_list = approxUniform(N=num_consumer_types,bot=bottomDiscFac,top=topDiscFac).X\n", "\n", "# Now, assign the discount factors we want to the ChineseConsumerTypes\n", "for j in range(num_consumer_types):\n", @@ -409,7 +409,22 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 5/5 [02:19<00:00, 27.87s/it]\n" + " 0%| | 0/5 [00:01\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mPermShkVarMultiplier\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mPermShkVarMultipliers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m \u001b[0mNatlSavingsRates\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcalcNatlSavingRate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mPermShkVarMultiplier\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mRNG_seed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m160\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mquarters_before_reform_to_plot\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 19\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;34m+=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m\u001b[0m in \u001b[0;36mcalcNatlSavingRate\u001b[0;34m(PrmShkVar_multiplier, RNG_seed)\u001b[0m\n\u001b[1;32m 93\u001b[0m \u001b[0mChineseConsumerTypeNew\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMrkvArray\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1.0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0.0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1.0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0.0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# Stay in low growth state\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 94\u001b[0m \u001b[0mChineseConsumerTypeNew\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minitializeSim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# Clear the history and make all newborn agents\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 95\u001b[0;31m \u001b[0mChineseConsumerTypeNew\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msimulate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m500\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# Simulate 500 quarders of data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 96\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 97\u001b[0m \u001b[0;31m# Now we want the high growth state to occur for the next 160 periods. We change the initial\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/projects/econ-ark/HARK/HARK/core.py\u001b[0m in \u001b[0;36msimulate\u001b[0;34m(self, sim_periods)\u001b[0m\n\u001b[1;32m 736\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 737\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msim_periods\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 738\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msimOnePeriod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 739\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mvar_name\u001b[0m \u001b[0;32min\u001b[0m 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\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPermGroFac\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;31m# and permanent growth factor\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 897\u001b[0;31m \u001b[0mIndices\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mIncomeDstnNow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# just a list of integers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 898\u001b[0m \u001b[0;31m# Get random draws of income shocks from the discrete distribution\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 899\u001b[0m EventDraws = DiscreteDistribution(\n", + "\u001b[0;31mTypeError\u001b[0m: 'DiscreteDistribution' object does not support indexing" ] } ], @@ -444,22 +459,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.ylabel('Natl Saving Rate')\n", "plt.xlabel('Quarters Since Economic Reforms')\n", diff --git a/notebooks/Chinese-Growth.py b/notebooks/Chinese-Growth.py index eedee468..cb53c313 100644 --- a/notebooks/Chinese-Growth.py +++ b/notebooks/Chinese-Growth.py @@ -174,7 +174,7 @@ bottomDiscFac = 0.9800 topDiscFac = 0.9934 -DiscFac_list = approxUniform(N=num_consumer_types,bot=bottomDiscFac,top=topDiscFac)[1] +DiscFac_list = approxUniform(N=num_consumer_types,bot=bottomDiscFac,top=topDiscFac).X # Now, assign the discount factors we want to the ChineseConsumerTypes for j in range(num_consumer_types): diff --git a/notebooks/IncExpectationExample.ipynb b/notebooks/IncExpectationExample.ipynb index fe7f5bdb..e21bf603 100644 --- a/notebooks/IncExpectationExample.ipynb +++ b/notebooks/IncExpectationExample.ipynb @@ -31,7 +31,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": { "code_folding": [], "lines_to_next_cell": 1 @@ -131,7 +131,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "metadata": { "code_folding": [ 1 @@ -175,7 +175,7 @@ " ThisDict['PrstIncCorr'] = CorrAct\n", " \n", " # Make a 7 point approximation to a uniform distribution of DiscFac\n", - " DiscFac_list = approxUniform(N=7,bot=DiscFac_center-DiscFac_spread,top=DiscFac_center+DiscFac_spread)[1]\n", + " DiscFac_list = approxUniform(N=7,bot=DiscFac_center-DiscFac_spread,top=DiscFac_center+DiscFac_spread).X\n", " \n", " type_list = []\n", " # Make a PersistentShockConsumerTypeX for each value of beta saved in DiscFac_list\n", @@ -244,41 +244,12 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": { "code_folding": [], "lines_to_next_cell": 2 }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The Lorenz curve for assests is\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The aggregate wealth to income ratio is 5.722878745432178\n", - "The Gini Coefficient for assests is 1.0\n", - "The average MPC by income quintile is [0.05911591741326721, 0.051551301726577924, 0.05000504062141127, 0.043722840170633326, 0.02782646242222285]\n" - ] - } - ], + "outputs": [], "source": [ "# Call the function with test values for (CorrAct, CorrPcvd, DiscFac_center, DiscFac_spread)\n", "AggWealthRatio, Lorenz, Gini, Avg_MPC = runRoszypalSchlaffmanExperiment(0.97, 0.9831, 0.9867, 0.0067)\n", diff --git a/notebooks/IncExpectationExample.py b/notebooks/IncExpectationExample.py index 93a2ccc1..217ce0ef 100644 --- a/notebooks/IncExpectationExample.py +++ b/notebooks/IncExpectationExample.py @@ -149,7 +149,7 @@ def runRoszypalSchlaffmanExperiment(CorrAct, CorrPcvd, DiscFac_center, DiscFac_s ThisDict['PrstIncCorr'] = CorrAct # Make a 7 point approximation to a uniform distribution of DiscFac - DiscFac_list = approxUniform(N=7,bot=DiscFac_center-DiscFac_spread,top=DiscFac_center+DiscFac_spread)[1] + DiscFac_list = approxUniform(N=7,bot=DiscFac_center-DiscFac_spread,top=DiscFac_center+DiscFac_spread).X type_list = [] # Make a PersistentShockConsumerTypeX for each value of beta saved in DiscFac_list diff --git a/notebooks/KrusellSmith.ipynb b/notebooks/KrusellSmith.ipynb index b7eb75be..74265dce 100644 --- a/notebooks/KrusellSmith.ipynb +++ b/notebooks/KrusellSmith.ipynb @@ -740,13 +740,13 @@ "outputs": [], "source": [ "# Construct the distribution of types\n", - "from HARK.utilities import approxUniform\n", + "from HARK.distribution import approxUniform\n", "\n", "# Specify the distribution of the discount factor\n", "num_types = 3 # number of types we want;\n", "DiscFac_mean = 0.9858 # center of beta distribution \n", "DiscFac_spread = 0.0085 # spread of beta distribution\n", - "DiscFac_dstn = approxUniform(num_types, DiscFac_mean-DiscFac_spread, DiscFac_mean+DiscFac_spread)[1]\n", + "DiscFac_dstn = approxUniform(num_types, DiscFac_mean-DiscFac_spread, DiscFac_mean+DiscFac_spread).X\n", "BaselineType = deepcopy(KSAgent)\n", "\n", "MyTypes = [] # initialize an empty list to hold our consumer types\n", diff --git a/notebooks/KrusellSmith.py b/notebooks/KrusellSmith.py index bd93749f..79d48f81 100644 --- a/notebooks/KrusellSmith.py +++ b/notebooks/KrusellSmith.py @@ -465,13 +465,13 @@ def in_ipynb(): # %% {"code_folding": []} # Construct the distribution of types -from HARK.utilities import approxUniform +from HARK.distribution import approxUniform # Specify the distribution of the discount factor num_types = 3 # number of types we want; DiscFac_mean = 0.9858 # center of beta distribution DiscFac_spread = 0.0085 # spread of beta distribution -DiscFac_dstn = approxUniform(num_types, DiscFac_mean-DiscFac_spread, DiscFac_mean+DiscFac_spread)[1] +DiscFac_dstn = approxUniform(num_types, DiscFac_mean-DiscFac_spread, DiscFac_mean+DiscFac_spread).X BaselineType = deepcopy(KSAgent) MyTypes = [] # initialize an empty list to hold our consumer types diff --git a/notebooks/Micro-and-Macro-Implications-of-Very-Impatient-HHs.ipynb b/notebooks/Micro-and-Macro-Implications-of-Very-Impatient-HHs.ipynb index fbf80a14..ee01318b 100644 --- a/notebooks/Micro-and-Macro-Implications-of-Very-Impatient-HHs.ipynb +++ b/notebooks/Micro-and-Macro-Implications-of-Very-Impatient-HHs.ipynb @@ -40,7 +40,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "code_folding": [ 25 @@ -213,14 +213,14 @@ "outputs": [], "source": [ "# This cell constructs seven instances of IndShockConsumerType with different discount factors\n", - "from HARK.utilities import approxUniform\n", + "from HARK.distribution import approxUniform\n", "BaselineType = IndShockConsumerType(**cstwMPC_calibrated_parameters)\n", "\n", "# Specify the distribution of the discount factor\n", "num_types = 7 # number of types we want\n", "DiscFac_mean = 0.9855583 # center of beta distribution \n", "DiscFac_spread = 0.0085 # spread of beta distribution\n", - "DiscFac_dstn = approxUniform(num_types, DiscFac_mean-DiscFac_spread, DiscFac_mean+DiscFac_spread)[1]\n", + "DiscFac_dstn = approxUniform(num_types, DiscFac_mean-DiscFac_spread, DiscFac_mean+DiscFac_spread).X\n", "\n", "MyTypes = [] # initialize an empty list to hold our consumer types\n", "for nn in range(num_types):\n", @@ -244,9 +244,129 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%| | 0/7 [00:00" ] @@ -345,7 +465,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -389,13 +509,16 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "\n", + "\n", + "\n", "The MPC at the 10th percentile of the distribution is 0.06\n", "The MPC at the 50th percentile of the distribution is 0.20\n", "The MPC at the 90th percentile of the distribution is 0.97\n" diff --git a/notebooks/Micro-and-Macro-Implications-of-Very-Impatient-HHs.py b/notebooks/Micro-and-Macro-Implications-of-Very-Impatient-HHs.py index 3d55cc38..73f032d9 100644 --- a/notebooks/Micro-and-Macro-Implications-of-Very-Impatient-HHs.py +++ b/notebooks/Micro-and-Macro-Implications-of-Very-Impatient-HHs.py @@ -7,7 +7,7 @@ # extension: .py # format_name: percent # format_version: '1.2' -# jupytext_version: 1.2.1 +# jupytext_version: 1.2.4 # kernelspec: # display_name: Python 3 # language: python @@ -186,14 +186,14 @@ def in_ipynb(): # %% # This cell constructs seven instances of IndShockConsumerType with different discount factors -from HARK.utilities import approxUniform +from HARK.distribution import approxUniform BaselineType = IndShockConsumerType(**cstwMPC_calibrated_parameters) # Specify the distribution of the discount factor num_types = 7 # number of types we want DiscFac_mean = 0.9855583 # center of beta distribution DiscFac_spread = 0.0085 # spread of beta distribution -DiscFac_dstn = approxUniform(num_types, DiscFac_mean-DiscFac_spread, DiscFac_mean+DiscFac_spread)[1] +DiscFac_dstn = approxUniform(num_types, DiscFac_mean-DiscFac_spread, DiscFac_mean+DiscFac_spread).X MyTypes = [] # initialize an empty list to hold our consumer types for nn in range(num_types): diff --git a/notebooks/Nondurables-During-Great-Recession.ipynb b/notebooks/Nondurables-During-Great-Recession.ipynb index aa2914db..2e8cf608 100644 --- a/notebooks/Nondurables-During-Great-Recession.ipynb +++ b/notebooks/Nondurables-During-Great-Recession.ipynb @@ -171,7 +171,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": { "code_folding": [ 0 @@ -185,7 +185,7 @@ "# Calibrations from cstwMPC\n", "bottomDiscFac = 0.9800\n", "topDiscFac = 0.9934\n", - "DiscFac_list = approxUniform(N=num_consumer_types,bot=bottomDiscFac,top=topDiscFac)[1]\n", + "DiscFac_list = approxUniform(N=num_consumer_types,bot=bottomDiscFac,top=topDiscFac).X\n", "\n", "# Now, assign the discount factors\n", "for j in range(num_consumer_types):\n", @@ -206,7 +206,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": { "code_folding": [ 0 @@ -218,7 +218,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|█████████████████████████████████████████████████████████| 7/7 [00:32<00:00, 4.71s/it]\n" + "100%|██████████| 7/7 [01:28<00:00, 12.68s/it]\n" ] } ], @@ -245,7 +245,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": { "code_folding": [ 0 @@ -290,7 +290,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": { "code_folding": [ 0 @@ -358,7 +358,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": { "code_folding": [ 0 @@ -387,7 +387,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": { "code_folding": [ 0 @@ -398,12 +398,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|███████████████████████████████████████████████████████| 10/10 [00:15<00:00, 1.54s/it]\n" + "100%|██████████| 10/10 [00:34<00:00, 3.42s/it]\n" ] }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] diff --git a/notebooks/Nondurables-During-Great-Recession.py b/notebooks/Nondurables-During-Great-Recession.py index 2d47a163..5799c8f3 100644 --- a/notebooks/Nondurables-During-Great-Recession.py +++ b/notebooks/Nondurables-During-Great-Recession.py @@ -131,7 +131,7 @@ # Calibrations from cstwMPC bottomDiscFac = 0.9800 topDiscFac = 0.9934 -DiscFac_list = approxUniform(N=num_consumer_types,bot=bottomDiscFac,top=topDiscFac)[1] +DiscFac_list = approxUniform(N=num_consumer_types,bot=bottomDiscFac,top=topDiscFac).X # Now, assign the discount factors for j in range(num_consumer_types): diff --git a/notebooks/Structural-Estimates-From-Empirical-MPCs-Fagereng-et-al.ipynb b/notebooks/Structural-Estimates-From-Empirical-MPCs-Fagereng-et-al.ipynb index 874880fd..a533df43 100644 --- a/notebooks/Structural-Estimates-From-Empirical-MPCs-Fagereng-et-al.ipynb +++ b/notebooks/Structural-Estimates-From-Empirical-MPCs-Fagereng-et-al.ipynb @@ -22,7 +22,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "code_folding": [ 0 @@ -41,7 +41,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": { "code_folding": [ 0 @@ -61,7 +61,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": { "code_folding": [ 0 @@ -81,7 +81,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": { "code_folding": [ 0 @@ -104,7 +104,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { "code_folding": [ 0 @@ -123,7 +123,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": { "code_folding": [ 0 @@ -138,7 +138,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": { "code_folding": [ 0 @@ -158,7 +158,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": { "code_folding": [ 0 @@ -190,7 +190,7 @@ " Euclidean distance between simulated MPCs and (adjusted) Table 9 MPCs.\n", " '''\n", " # Give our consumer types the requested discount factor distribution\n", - " beta_set = approxUniform(N=TypeCount,bot=center-spread,top=center+spread)[1]\n", + " beta_set = approxUniform(N=TypeCount,bot=center-spread,top=center+spread).X\n", " for j in range(TypeCount):\n", " EstTypeList[j](DiscFac = beta_set[j])\n", "\n", @@ -256,13 +256,117 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": { "code_folding": [ 0 ] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.92 0.03 1.1274854942184123\n", + "0.9660000000000001 0.03 1.8595059870322312\n", + "0.92 0.0315 1.1283488603479197\n", + "0.874 0.0315 0.778255871453904\n", + "0.8280000000000001 0.03225 0.6819825156919915\n", + "0.8280000000000002 0.03075 0.6835548812203114\n", + "0.7360000000000001 0.033 0.7922890441302506\n", + "0.782 0.03225 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0.5034386878700592\n", + "0.775890624999966 0.17442773437499942 0.5149023099777296\n", + "0.7936796874999648 0.17777050781249937 0.5315857186141032\n", + "0.791523437499972 0.1482744140624995 0.5061530602276895\n", + "0.7929609374999671 0.1679384765624994 0.5082715311300752\n", + "0.7918828124999708 0.15319042968749946 0.5033949436309413\n", + "0.7782265624999698 0.15838183593749947 0.512748835721533\n", + "0.7938593749999701 0.15622851562499945 0.5028877731574135\n", + "0.8003281249999719 0.14871679687499945 0.5068348206273852\n", + "0.7891425781249697 0.15770581054687446 0.502519441768042\n", + "0.7911191406249689 0.16074389648437443 0.5022974415782882\n", + "0.7907373046874677 0.1645206298828119 0.5032698407410588\n", + "0.7864023437499685 0.1622211914062494 0.5027664061147642\n", + "0.7882666015624689 0.16072302246093692 0.502198620174269\n", + "0.7902431640624681 0.16376110839843686 0.5026328874780666\n", + "0.7894177246093443 0.15921963500976505 0.5021703660917155\n", + "0.7865651855468443 0.15919876098632751 0.5030573631478745\n", + "0.7899806518554378 0.1603576126098627 0.502160531034932\n", + "0.7911317749023132 0.15885422515869083 0.5022769519932859\n", + "0.78898289489743 0.1602558231353754 0.502180579074867\n", + "0.7904154815673521 0.15932142448425235 0.5021833317477105\n", + "0.7893410415649105 0.16002222347259465 0.5021165086178974\n", + "0.7899039688110039 0.1611602010726923 0.5021112183082238\n", + "0.7901470909118338 0.16213048410415593 0.5022990867023371\n", + "0.7892643585204766 0.16082481193542425 0.502159403561549\n", + "0.7894434318542168 0.16070801210403385 0.5021404531991109\n", + "0.7898015785216975 0.1604744124412531 0.50213329203524\n", + "0.7897120418548274 0.16053281235694827 0.5021302497927074\n", + "0.789532968521087 0.16064961218833868 0.5021304652782825\n", + "0.7896672735213923 0.1605620123147959 0.502126210894908\n", + "0.789577736854522 0.16062041223049106 0.5021314791648644\n", + "0.7896448893546748 0.16057661229371967 0.5021356844024427\n", + "0.7896225051879572 0.16059121227264347 0.5021256061827057\n", + "0.7897856211661981 0.1608611066937441 0.5021324277978072\n", + "0.7897408528327629 0.16089030665159165 0.5021362355798749\n", + "0.7897744290828392 0.16086840668320598 0.5021389064710859\n", + "0.7897632369994805 0.16087570667266787 0.502129658550051\n", + "0.7898447949886009 0.1610106538832182 0.5021074951519786\n", + "0.7899855268001243 0.1612951482832426 0.5021245728056754\n", + "0.7899299543499633 0.16119028788059891 0.5021135412553295\n", + "0.7898188094496416 0.16098056707531155 0.5021025392131131\n", + "0.7897632369994805 0.16087570667266787 0.502129658550051\n", + "0.7897596356272387 0.16083101988583742 0.5021341894826656\n", + "0.7898678855150626 0.16107790577597858 0.5021133757712383\n", + "0.7898318022191213 0.16099561047926486 0.5021068384032057\n", + "0.7898613891303228 0.16107038407400193 0.5021036662483996\n", + "Optimization terminated successfully.\n", + " Current function value: 0.502103\n", + " Iterations: 41\n", + " Function evaluations: 85\n", + "Time to estimate is 188.1969873905182 seconds.\n", + "Finished estimating for scaling factor of 1.0 and \"splurge amount\" of $0.0\n", + "Optimal (beta,nabla) is [0.78981881 0.16098057], simulated MPCs are:\n", + "[[0.77361336 0.68317127 0.56461082 0.40476962]\n", + " [0.74354975 0.66482752 0.55301552 0.39626053]\n", + " [0.70353353 0.63512154 0.5305429 0.3793119 ]\n", + " [0.5613238 0.50428804 0.4125933 0.29261249]]\n", + "Distance from Fagereng et al Table 9 is 0.5021025392131131\n" + ] + } + ], "source": [ "# Conduct the estimation\n", "\n", @@ -277,7 +381,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -286,7 +390,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ diff --git a/notebooks/Structural-Estimates-From-Empirical-MPCs-Fagereng-et-al.py b/notebooks/Structural-Estimates-From-Empirical-MPCs-Fagereng-et-al.py index 78d353b5..dc98bd0b 100644 --- a/notebooks/Structural-Estimates-From-Empirical-MPCs-Fagereng-et-al.py +++ b/notebooks/Structural-Estimates-From-Empirical-MPCs-Fagereng-et-al.py @@ -120,7 +120,7 @@ def FagerengObjFunc(center,spread,verbose=False): Euclidean distance between simulated MPCs and (adjusted) Table 9 MPCs. ''' # Give our consumer types the requested discount factor distribution - beta_set = approxUniform(N=TypeCount,bot=center-spread,top=center+spread)[1] + beta_set = approxUniform(N=TypeCount,bot=center-spread,top=center+spread).X for j in range(TypeCount): EstTypeList[j](DiscFac = beta_set[j])