Add a couple missing constructors

If there's anything else that can be converted to a constructor, I don't know about it.
econ-ark · Feb 17, 2025 · df5a130 · df5a130
1 parent 0f12398
commit df5a130
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Showing 3 changed files with 73 additions and 21 deletions.
diff --git a/HARK/ConsumptionSaving/ConsAggShockModel.py b/HARK/ConsumptionSaving/ConsAggShockModel.py
@@ -127,6 +127,38 @@ def make_aggmrkv_solution_terminal(CRRA, MrkvArray):
     return solution_terminal
 
 
+def make_exponential_MgridBase(MaggCount, MaggPerturb, MaggExpFac):
+    """
+    Constructor function for MgridBase, the grid of aggregate market resources
+    relative to the steady state. This grid is always centered around 1.0.
+
+    Parameters
+    ----------
+    MaggCount : int
+        Number of gridpoints for aggregate market resources. Should be odd.
+    MaggPerturb : float
+        Small perturbation around the steady state; the grid will always include
+        1+perturb and 1-perturb.
+    MaggExpFac : float
+        Log growth factor for gridpoints beyond the two adjacent to the steady state.
+
+    Returns
+    -------
+    MgridBase : np.array
+        Grid of aggregate market resources relative to the steady state.
+    """
+    N = int((MaggCount - 1) / 2)
+    gridpoints = [1.0 - MaggPerturb, 1.0, 1.0 + MaggPerturb]
+    fac = np.exp(MaggExpFac)
+    for n in range(N - 1):
+        new_hi = gridpoints[-1] * fac
+        new_lo = gridpoints[0] / fac
+        gridpoints.append(new_hi)
+        gridpoints.insert(0, new_lo)
+    MgridBase = np.array(gridpoints)
+    return MgridBase
+
+
 ###############################################################################
 
 
@@ -879,6 +911,7 @@ def solve_KrusellSmith(
     "PermShkDstn": get_PermShkDstn_from_IncShkDstn,
     "TranShkDstn": get_TranShkDstn_from_IncShkDstn,
     "aXtraGrid": make_assets_grid,
+    "MgridBase": make_exponential_MgridBase,
     "solution_terminal": make_aggshock_solution_terminal,
 }
 
@@ -904,11 +937,12 @@ def solve_KrusellSmith(
     "aXtraExtra": None,  # Additional other values to add in grid (optional)
 }
 
-# Choose a grid of capital-to-labor-ratios (factors relative to SS)
-# TODO: Make a constructor for this
-MgridBase = np.array(
-    [0.1, 0.3, 0.6, 0.8, 0.9, 0.98, 1.0, 1.02, 1.1, 1.2, 1.6, 2.0, 3.0]
-)
+# Default parameters to make MgridBase using make_exponential_MgridBase
+default_MgridBase_params = {
+    "MaggCount": 17,
+    "MaggPerturb": 0.01,
+    "MaggExpFac": 0.15,
+}
 
 # Make a dictionary to specify an aggregate income shocks consumer type
 init_agg_shocks = {
@@ -923,7 +957,6 @@ def solve_KrusellSmith(
     "LivPrb": [0.98],  # Survival probability after each period
     "PermGroFac": [1.00],  # Permanent income growth factor
     "BoroCnstArt": 0.0,  # Artificial borrowing constraint
-    "MgridBase": MgridBase,
     # 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
@@ -941,6 +974,7 @@ def solve_KrusellSmith(
 }
 init_agg_shocks.update(default_IncShkDstn_params)
 init_agg_shocks.update(default_aXtraGrid_params)
+init_agg_shocks.update(default_MgridBase_params)
 
 
 class AggShockConsumerType(IndShockConsumerType):
@@ -1545,6 +1579,7 @@ def get_RnextArray(transition_arrays):
     "mNextArray": get_mNextArray,
     "MnextArray": get_MnextArray,
     "RnextArray": get_RnextArray,
+    "MgridBase": make_exponential_MgridBase,
 }
 
 init_KS_agents = {
@@ -1558,9 +1593,10 @@ def get_RnextArray(transition_arrays):
     "aMax": 50.0,
     "aCount": 32,
     "aNestFac": 2,
-    "MgridBase": np.array(
-        [0.1, 0.3, 0.6, 0.8, 0.9, 0.95, 0.98, 1.0, 1.02, 1.05, 1.1, 1.2, 1.6, 2.0, 3.0]
-    ),
+    "MaggCount": 25,
+    "MaggPerturb": 0.01,
+    "MaggExpFac": 0.12,
+    "MgridBase": np.array([0.99, 1.0, 1.01]),  ## dummy, this will be overwritten
     "AgentCount": 5000,
 }
 
@@ -1598,11 +1634,14 @@ def __init__(self, **kwds):
         temp = kwds.copy()
         temp["construct"] = False
         AgentType.__init__(self, **temp)
+        self.construct("MgridBase")
+
         # Special case: this type *must* be initialized with construct=False
         # because the data required to make its solution attributes is obtained
         # from the associated economy, not passed as part of its parameters.
         # To make it work properly, instantiate both this class and an instance
         # of KrusellSmithEconomy, then use this class' get_economy_data method.
+        # Exception: MgridBase must exist
 
     def pre_solve(self):
         self.construct("solution_terminal")

diff --git a/HARK/ConsumptionSaving/ConsRepAgentModel.py b/HARK/ConsumptionSaving/ConsRepAgentModel.py
@@ -16,7 +16,10 @@
     IndShockConsumerType,
     make_basic_CRRA_solution_terminal,
 )
-from HARK.ConsumptionSaving.ConsMarkovModel import MarkovConsumerType
+from HARK.ConsumptionSaving.ConsMarkovModel import (
+    MarkovConsumerType,
+    make_simple_binary_markov,
+)
 from HARK.distributions import MarkovProcess
 from HARK.interpolation import LinearInterp, MargValueFuncCRRA
 from HARK.utilities import make_assets_grid
@@ -61,6 +64,14 @@ def make_repagent_markov_solution_terminal(CRRA, MrkvArray):
     return solution_terminal
 
 
+def make_simple_binary_rep_markov(Mrkv_p11, Mrkv_p22):
+    MrkvArray = make_simple_binary_markov(1, [Mrkv_p11], [Mrkv_p22])[0]
+    return MrkvArray
+
+
+###############################################################################
+
+
 def solve_ConsRepAgent(
     solution_next, DiscFac, CRRA, IncShkDstn, CapShare, DeprFac, PermGroFac, aXtraGrid
 ):
@@ -396,10 +407,12 @@ def get_states(self):
 markov_repagent_constructor_dict["solution_terminal"] = (
     make_repagent_markov_solution_terminal
 )
+markov_repagent_constructor_dict["MrkvArray"] = make_simple_binary_rep_markov
 
 init_markov_rep_agent = init_rep_agent.copy()
 init_markov_rep_agent["PermGroFac"] = [[0.97, 1.03]]
-init_markov_rep_agent["MrkvArray"] = np.array([[0.99, 0.01], [0.01, 0.99]])
+init_markov_rep_agent["Mrkv_p11"] = 0.99
+init_markov_rep_agent["Mrkv_p22"] = 0.99
 init_markov_rep_agent["Mrkv"] = 0
 init_markov_rep_agent["constructors"] = markov_repagent_constructor_dict
 

diff --git a/HARK/ConsumptionSaving/tests/test_ConsAggShockModel.py b/HARK/ConsumptionSaving/tests/test_ConsAggShockModel.py
@@ -42,7 +42,7 @@ def test_agent(self):
         self.agents[0].solve()
         self.assertAlmostEqual(
             self.agents[0].solution[0].cFunc(10.0, self.economy.MSS),
-            3.22908,
+            3.23694,
             places=HARK_PRECISION,
         )
 
@@ -58,7 +58,7 @@ def test_macro(self):
         self.economy.solve()  # Solve for the general equilibrium of the economy
 
         self.economy.AFunc = self.economy.dynamics.AFunc
-        self.assertAlmostEqual(self.economy.AFunc.slope, 1.11810, places=HARK_PRECISION)
+        self.assertAlmostEqual(self.economy.AFunc.slope, 1.13204, places=HARK_PRECISION)
 
         # simulation test -- seed/generator specific
         # self.assertAlmostEqual(self.economy.history["MaggNow"][10], 7.45632, place = HARK_PRECISION)
@@ -80,7 +80,7 @@ def test_agent(self):
         self.agent.solve()
         self.assertAlmostEqual(
             self.agent.solution[0].cFunc[0](10.0, self.economy.MSS),
-            2.56359,
+            2.56379,
             places=HARK_PRECISION,
         )
 
@@ -96,7 +96,7 @@ def test_economy(self):
 
         self.economy.AFunc = self.economy.dynamics.AFunc
         self.assertAlmostEqual(
-            self.economy.AFunc[0].slope, 1.08987, places=HARK_PRECISION
+            self.economy.AFunc[0].slope, 1.08571, places=HARK_PRECISION
         )
 
         # simulation test -- seed/generator specific
@@ -127,7 +127,7 @@ def test_agent(self):
         self.agent.solve()
         self.assertAlmostEqual(
             self.agent.solution[0].cFunc[0](10.0, self.economy.MSS),
-            1.23868,
+            1.24280,
             places=HARK_PRECISION,
         )
 
@@ -177,7 +177,7 @@ def test_methods(self):
 
         # testing precompute_arrays()
         self.assertAlmostEqual(
-            self.agent.mNextArray[5, 2, 3, 0], 0.34880, places=HARK_PRECISION
+            self.agent.mNextArray[5, 2, 3, 0], 0.35541, places=HARK_PRECISION
         )
 
         # testing make_grid()
@@ -192,7 +192,7 @@ def test_methods(self):
 
         self.assertAlmostEqual(
             self.economy.agents[0].solution[0].cFunc[0](10, self.economy.MSS).tolist(),
-            1.2386774,
+            1.24280,
             places=4,
         )
 
@@ -234,11 +234,11 @@ def test_methods(self):
         new_dynamics = self.economy.update_dynamics()
 
         self.assertAlmostEqual(
-            new_dynamics.AFunc[0].slope, 1.01779, places=HARK_PRECISION
+            new_dynamics.AFunc[0].slope, 1.02231, places=HARK_PRECISION
         )
 
         self.assertAlmostEqual(
-            new_dynamics.AFunc[1].slope, 1.02020, places=HARK_PRECISION
+            new_dynamics.AFunc[1].slope, 1.02515, places=HARK_PRECISION
         )
 
 
@@ -256,7 +256,7 @@ def test_economy(self):
 
         self.economy.AFunc = self.economy.dynamics.AFunc
         self.assertAlmostEqual(
-            self.economy.AFunc[0].slope, 1.02668, places=HARK_PRECISION
+            self.economy.AFunc[0].slope, 1.03346, places=HARK_PRECISION
         )
 
         # simulation test -- seed/generator specific