Merge branch 'econ-ark:master' into distribution/updates

HARK/ConsumptionSaving/ConsBequestModel.py

@@ -273,12 +273,8 @@ def make_warmglow_portfolio_solution_terminal(
 
 class BequestWarmGlowConsumerType(IndShockConsumerType):
     r"""
-    A consumer type with idiosyncratic shocks to permanent and transitory income.
-    Their problem is defined by a sequence of income distributions, survival probabilities
-    (:math:`1-\mathsf{D}`), and permanent income growth rates (:math:`\Gamma`), as well
-    as time invariant values for risk aversion (:math:`\rho`), discount factor (:math:`\beta`),
-    the interest rate (:math:`\mathsf{R}`), the grid of end-of-period assets, and an artificial
-    borrowing constraint (:math:`\underline{a}`).
+    A consumer type with based on IndShockConsumerType, with an additional bequest motive.
+    They gain utility for any wealth they leave when they die, according to a Stone-Geary utility.
 
     .. math::
         \newcommand{\CRRA}{\rho}
@@ -295,7 +291,7 @@ class BequestWarmGlowConsumerType(IndShockConsumerType):
         (\psi_{t+1},\theta_{t+1}) &\sim F_{t+1}, \\
         \mathbb{E}[\psi]=\mathbb{E}[\theta] &= 1, \\
         u(c) &= \frac{c^{1-\CRRA}}{1-\CRRA} \\
-        u_{Beq} (a) = \textbf{BeqFac} \frac{(a+\textbf{BeqShift})^{1-\CRRA_{Beq}}}{1-\CRRA_{Beq}}
+        u_{Beq} (a) &= \textbf{BeqFac} \frac{(a+\textbf{BeqShift})^{1-\CRRA_{Beq}}}{1-\CRRA_{Beq}} \\
         \end{align*}
 
 
@@ -1277,7 +1273,7 @@ def calc_EndOfPrd_v(S, a, z):
 
 class BequestWarmGlowPortfolioType(PortfolioConsumerType):
     r"""
-    A consumer type with based on IndShockConsumerType, with an additional bequest motive.
+    A consumer type with based on PortfolioConsumerType, with an additional bequest motive.
     They gain utility for any wealth they leave when they die, according to a Stone-Geary utility.
 
     .. math::
@@ -1300,7 +1296,7 @@ class BequestWarmGlowPortfolioType(PortfolioConsumerType):
         (\psi_{t+1},\theta_{t+1},\phi_{t+1},p_t) &\sim F_{t+1}, \\
         \mathbb{E}[\psi]=\mathbb{E}[\theta] &= 1. \\
         u(c) &= \frac{c^{1-\CRRA}}{1-\CRRA} \\
-        u_{Beq} (a) = \textbf{BeqFac} \frac{(a+\textbf{BeqShift})^{1-\CRRA_{Beq}}}{1-\CRRA_{Beq}}
+        u_{Beq} (a) &= \textbf{BeqFac} \frac{(a+\textbf{BeqShift})^{1-\CRRA_{Beq}}}{1-\CRRA_{Beq}} \\
         \end{align*}
 
 

HARK/ConsumptionSaving/ConsIndShockModel.py

@@ -2798,7 +2798,7 @@ class KinkedRconsumerType(IndShockConsumerType):
         \end{cases}\\
         \Rfree_{boro} &> \Rfree_{save}, \\
         (\psi_{t+1},\theta_{t+1}) &\sim F_{t+1}, \\
-        \mathbb{E}[\psi]=\mathbb{E}[\theta] &= 1.
+        \mathbb{E}[\psi]=\mathbb{E}[\theta] &= 1.\\
         u(c) &= \frac{c^{1-\CRRA}}{1-\CRRA} \\
         \end{align*}
 

HARK/ConsumptionSaving/ConsWealthPortfolioModel.py

@@ -3,8 +3,8 @@
 import numpy as np
 from HARK.ConsumptionSaving.ConsPortfolioModel import (
     PortfolioConsumerType,
-    init_portfolio,
     PortfolioSolution,
+    make_portfolio_solution_terminal,
 )
 from HARK.distributions import expected
 from HARK.interpolation import (
@@ -14,11 +14,22 @@
     MargValueFuncCRRA,
     ValueFuncCRRA,
 )
+from HARK.Calibration.Assets.AssetProcesses import (
+    make_lognormal_RiskyDstn,
+    combine_IncShkDstn_and_RiskyDstn,
+    calc_ShareLimit_for_CRRA,
+)
+from HARK.Calibration.Income.IncomeProcesses import (
+    construct_lognormal_income_process_unemployment,
+    get_PermShkDstn_from_IncShkDstn,
+    get_TranShkDstn_from_IncShkDstn,
+)
+from HARK.ConsumptionSaving.ConsRiskyAssetModel import (
+    make_simple_ShareGrid,
+)
 from HARK.rewards import UtilityFuncCRRA
-from HARK.utilities import NullFunc
+from HARK.utilities import NullFunc, make_assets_grid
 
-
-import numpy as np
 from HARK.interpolation import LinearInterp
 
 
@@ -100,6 +111,129 @@ def __call__(self, omega):
         return np.nan_to_num(chi)
 
 
+# Trivial constructor function
+def make_ChiFromOmega_function(CRRA, WealthShare, ChiFromOmega_N, ChiFromOmega_bound):
+    return ChiFromOmegaFunction(
+        CRRA, WealthShare, N=ChiFromOmega_N, z_bound=ChiFromOmega_bound
+    )
+
+
+###############################################################################
+
+# Make a dictionary of constructors for the wealth-in-utility portfolio choice consumer type
+WealthPortfolioConsumerType_constructors_default = {
+    "IncShkDstn": construct_lognormal_income_process_unemployment,
+    "PermShkDstn": get_PermShkDstn_from_IncShkDstn,
+    "TranShkDstn": get_TranShkDstn_from_IncShkDstn,
+    "aXtraGrid": make_assets_grid,
+    "RiskyDstn": make_lognormal_RiskyDstn,
+    "ShockDstn": combine_IncShkDstn_and_RiskyDstn,
+    "ShareLimit": calc_ShareLimit_for_CRRA,
+    "ShareGrid": make_simple_ShareGrid,
+    "ChiFunc": make_ChiFromOmega_function,
+    "solution_terminal": make_portfolio_solution_terminal,
+}
+
+# Default parameters to make IncShkDstn using construct_lognormal_income_process_unemployment
+WealthPortfolioConsumerType_IncShkDstn_default = {
+    "PermShkStd": [0.1],  # Standard deviation of log permanent income shocks
+    "PermShkCount": 7,  # Number of points in discrete approximation to permanent income shocks
+    "TranShkStd": [0.1],  # Standard deviation of log transitory income shocks
+    "TranShkCount": 7,  # Number of points in discrete approximation to transitory income shocks
+    "UnempPrb": 0.05,  # Probability of unemployment while working
+    "IncUnemp": 0.3,  # Unemployment benefits replacement rate while working
+    "T_retire": 0,  # Period of retirement (0 --> no retirement)
+    "UnempPrbRet": 0.005,  # Probability of "unemployment" while retired
+    "IncUnempRet": 0.0,  # "Unemployment" benefits when retired
+}
+
+# Default parameters to make aXtraGrid using make_assets_grid
+WealthPortfolioConsumerType_aXtraGrid_default = {
+    "aXtraMin": 0.001,  # Minimum end-of-period "assets above minimum" value
+    "aXtraMax": 100,  # Maximum end-of-period "assets above minimum" value
+    "aXtraNestFac": 1,  # Exponential nesting factor for aXtraGrid
+    "aXtraCount": 200,  # Number of points in the grid of "assets above minimum"
+    "aXtraExtra": None,  # Additional other values to add in grid (optional)
+}
+
+# Default parameters to make RiskyDstn with make_lognormal_RiskyDstn (and uniform ShareGrid)
+WealthPortfolioConsumerType_RiskyDstn_default = {
+    "RiskyAvg": 1.08,  # Mean return factor of risky asset
+    "RiskyStd": 0.18362634887,  # Stdev of log returns on risky asset
+    "RiskyCount": 5,  # Number of integration nodes to use in approximation of risky returns
+}
+
+WealthPortfolioConsumerType_ShareGrid_default = {
+    "ShareCount": 25  # Number of discrete points in the risky share approximation
+}
+
+# Default parameters to make ChiFunc with make_ChiFromOmega_function
+WealthPortfolioConsumerType_ChiFunc_default = {
+    "ChiFromOmega_N": 501,  # Number of gridpoints in chi-from-omega function
+    "ChiFromOmega_bound": 15,  # Highest gridpoint to use for it
+}
+
+# Make a dictionary to specify a risky asset consumer type
+WealthPortfolioConsumerType_solving_default = {
+    # BASIC HARK PARAMETERS REQUIRED TO SOLVE THE MODEL
+    "cycles": 1,  # Finite, non-cyclic model
+    "T_cycle": 1,  # Number of periods in the cycle for this agent type
+    "constructors": WealthPortfolioConsumerType_constructors_default,  # See dictionary above
+    # PRIMITIVE RAW PARAMETERS REQUIRED TO SOLVE THE MODEL
+    "CRRA": 5.0,  # Coefficient of relative risk aversion
+    "Rfree": 1.03,  # Return factor on risk free asset
+    "DiscFac": 0.90,  # Intertemporal discount factor
+    "LivPrb": [0.98],  # Survival probability after each period
+    "PermGroFac": [1.01],  # Permanent income growth factor
+    "BoroCnstArt": 0.0,  # Artificial borrowing constraint
+    "WealthShare": 0.5,  # Share of wealth in Cobb-Douglas aggregator in utility function
+    "WealthShift": 0.1,  # Shifter for wealth in utility function
+    "DiscreteShareBool": False,  # Whether risky asset share is restricted to discrete values
+    "vFuncBool": False,  # Whether to calculate the value function during solution
+    "CubicBool": False,  # Whether to use cubic spline interpolation when True
+    # (Uses linear spline interpolation for cFunc when False)
+    "AdjustPrb": 1.0,  # Probability that the agent can update their risky portfolio share each period
+    "sim_common_Rrisky": True,  # Whether risky returns have a shared/common value across agents
+}
+WealthPortfolioConsumerType_simulation_default = {
+    # PARAMETERS REQUIRED TO SIMULATE THE MODEL
+    "AgentCount": 10000,  # Number of agents of this type
+    "T_age": None,  # Age after which simulated agents are automatically killed
+    "aNrmInitMean": 0.0,  # Mean of log initial assets
+    "aNrmInitStd": 1.0,  # Standard deviation of log initial assets
+    "pLvlInitMean": 0.0,  # Mean of log initial permanent income
+    "pLvlInitStd": 0.0,  # Standard deviation of log initial permanent income
+    "PermGroFacAgg": 1.0,  # Aggregate permanent income growth factor
+    # (The portion of PermGroFac attributable to aggregate productivity growth)
+    "NewbornTransShk": False,  # Whether Newborns have transitory shock
+    # ADDITIONAL OPTIONAL PARAMETERS
+    "PerfMITShk": False,  # Do Perfect Foresight MIT Shock
+    # (Forces Newborns to follow solution path of the agent they replaced if True)
+    "neutral_measure": False,  # Whether to use permanent income neutral measure (see Harmenberg 2021)
+}
+
+# Assemble the default dictionary
+WealthPortfolioConsumerType_default = {}
+WealthPortfolioConsumerType_default.update(WealthPortfolioConsumerType_solving_default)
+WealthPortfolioConsumerType_default.update(
+    WealthPortfolioConsumerType_simulation_default
+)
+WealthPortfolioConsumerType_default.update(
+    WealthPortfolioConsumerType_aXtraGrid_default
+)
+WealthPortfolioConsumerType_default.update(
+    WealthPortfolioConsumerType_ShareGrid_default
+)
+WealthPortfolioConsumerType_default.update(
+    WealthPortfolioConsumerType_IncShkDstn_default
+)
+WealthPortfolioConsumerType_default.update(
+    WealthPortfolioConsumerType_RiskyDstn_default
+)
+WealthPortfolioConsumerType_default.update(WealthPortfolioConsumerType_ChiFunc_default)
+init_wealth_portfolio = WealthPortfolioConsumerType_default
+
+
 class WealthPortfolioConsumerType(PortfolioConsumerType):
     time_inv_ = deepcopy(PortfolioConsumerType.time_inv_)
     time_inv_ = time_inv_ + ["WealthShare", "WealthShift", "ChiFunc"]
@@ -114,10 +248,18 @@ def __init__(self, **kwds):
 
         self.solve_one_period = solve_one_period_WealthPortfolio
 
+    def update(self):
+        super().update()
+        self.update_ChiFunc()
+
+    def update_ChiFunc(self):
         if self.WealthShare == 0.0:
             self.ChiFunc = None
         else:
-            self.ChiFunc = ChiFromOmegaFunction(self.CRRA, self.WealthShare)
+            self.construct("ChiFunc")
+
+
+###############################################################################
 
 
 def utility(c, a, CRRA, share=0.0, intercept=0.0):
@@ -502,8 +644,3 @@ def solve_one_period_WealthPortfolio(
         vFuncAdj=vFuncNow,
     )
     return solution_now
-
-
-init_wealth_portfolio = init_portfolio.copy()
-init_wealth_portfolio["WealthShare"] = 0.5
-init_wealth_portfolio["WealthShift"] = 0.1

HARK/distribution.py

@@ -963,11 +963,11 @@ def _approx_equiprobable(
             )
 
         if np.array_equal(self.Sigma, np.diag(np.diag(self.Sigma))):
-            ind_atoms = np.empty((self.M, N))
+            ind_atoms = np.empty((self.M, N + 2 * tail_N))
 
             for i in range(self.M):
                 if self.Sigma[i, i] == 0.0:
-                    x_atoms = np.repeat(np.exp(self.mu[i]), N)
+                    x_atoms = np.repeat(np.exp(self.mu[i]), N + 2 * tail_N)
                     ind_atoms[i] = x_atoms
                 else:
                     x_atoms = (
@@ -983,7 +983,22 @@ def _approx_equiprobable(
             atoms = np.stack(
                 [ar.flatten() for ar in list(np.meshgrid(*atoms_list))], axis=1
             ).T
-            pmv = np.repeat(1 / (N**self.M), N**self.M)
+
+            interiors = np.empty([self.M, (N + 2 * tail_N) ** (self.M)])
+
+            inners = np.zeros(N + 2 * tail_N)
+
+            if tail_N > 0:
+                inners[:tail_N] = [(tail_N - i) for i in range(tail_N)]
+                inners[-tail_N:] = [(i + 1) for i in range(tail_N)]
+
+            for i in range(self.M):
+                inners_i = [inners for _ in range((N + 2 * tail_N) ** i)]
+
+                interiors[i] = np.repeat(
+                    [*inners_i], (N + 2 * tail_N) ** (self.M - (i + 1))
+                )
+
         else:
             if tail_bound is not None:
                 if type(tail_bound) is float:
@@ -1024,10 +1039,10 @@ def eval(params, z):
                 excl = []
 
                 for j in range(len(z)):
-                    if z[j, 0] != z[j, 1]:
-                        inds.append(j)
-                    else:
+                    if z[j, 0] == z[j, 1]:
                         excl.append(j)
+                    elif params[j] != 0.0:
+                        inds.append(j)
 
                 dim = len(inds)
 
@@ -1111,21 +1126,21 @@ def eval(params, z):
 
                     atoms[i] = xi_atoms
 
-            max_locs = np.argmax(np.abs(interiors), axis=0)
+        max_locs = np.argmax(np.abs(interiors), axis=0)
 
-            max_inds = np.stack([max_locs, np.arange(len(max_locs))], axis=1)
+        max_inds = np.stack([max_locs, np.arange(len(max_locs))], axis=1)
 
-            prob_locs = interiors[max_inds[:, 0], max_inds[:, 1]]
+        prob_locs = interiors[max_inds[:, 0], max_inds[:, 1]]
 
-            def prob_assign(x):
-                if x == 0:
-                    return 1 / (N**self.M)
-                else:
-                    return 0.0
+        def prob_assign(x):
+            if x == 0:
+                return 1 / (N**self.M)
+            else:
+                return 0.0
 
-            prob_vec = np.vectorize(prob_assign)
+        prob_vec = np.vectorize(prob_assign)
 
-            pmv = prob_vec(prob_locs)
+        pmv = prob_vec(prob_locs)
 
         limit = {
             "dist": self,

binder/requirements.txt

@@ -1,8 +1,2 @@
-cite2c
 git+https://github.com/econ-ark/hark@master
-ipywidgets
-jupyter_contrib_nbextensions
-matplotlib
-numpy
-scipy
-seaborn
+sequence_jacobian # required for KS-HARK-presentation example