Generic monte carlo simulation

Documentation/CHANGELOG.md

@@ -16,6 +16,7 @@ Release Date: TBD
 
 - Adds `HARK.core.AgentPopulation` class to represent a population of agents with ex-ante heterogeneous parametrizations as distributions. [#1237](https://github.com/econ-ark/HARK/pull/1237)
 - Adds `HARK.core.Parameters` class to represent a collection of time varying and time invariant parameters in a model. [#1240](https://github.com/econ-ark/HARK/pull/1240)
+- Adds `HARK.simulation.monte_carlo` module for generic Monte Carlo simulation functions using Python model configurations. [1296](https://github.com/econ-ark/HARK/pull/1296)
 
 ### Minor Changes
 
@@ -26,6 +27,8 @@ Release Date: TBD
 - Fixes bug that prevented risky-asset consumer types from working with time-varying interest rates `Rfree`. [1343](https://github.com/econ-ark/HARK/pull/1343)
 - Overhauls and expands condition checking for the ConsIndShock model [#1294](https://github.com/econ-ark/HARK/pull/1294). Condition values and a description of their interpretation is stored in the bilt dictionary of IndShockConsumerType.
 - Creates a `models/` directory with Python model configurations for perfect foresight and Fisher 2-period models. [1347](https://github.com/econ-ark/HARK/pull/1347)
+- Fixes bug in AgentType simulations where 'who_dies' for period t was being recorded in period t-1in the history Carlo simulation functions using Python model configurations.[1296](https://github.com/econ-ark/HARK/pull/1296)
+- Removes unused `simulation.py` .[1296](https://github.com/econ-ark/HARK/pull/1296)
 
 ### 0.13.0
 

Documentation/reference/index.rst

@@ -13,6 +13,7 @@ API Reference
    tools/frame
    tools/helpers
    tools/interpolation
+   tools/model
    tools/numba_tools
    tools/parallel
    tools/rewards

Documentation/reference/tools/model.rst

@@ -0,0 +1,7 @@
+Model
+-------------
+
+.. automodule:: HARK.model
+   :members:
+   :undoc-members:
+   :show-inheritance:

Documentation/reference/tools/simulation.rst

@@ -1,7 +1,7 @@
 Simulation
 ------------
 
-.. automodule:: HARK.simulation
+.. automodule:: HARK.simulation.monte_carlo
    :members:
    :undoc-members:
    :show-inheritance:

HARK/core.py

@@ -7,6 +7,7 @@
 problem by finding a general equilibrium dynamic rule.
 """
 # Set logging and define basic functions
+# Set logging and define basic functions
 import logging
 import sys
 from collections import defaultdict, namedtuple
@@ -1061,7 +1062,14 @@
                     elif var_name in self.controls:
                         self.history[var_name][self.t_sim, :] = self.controls[var_name]
                     else:
-                        self.history[var_name][self.t_sim, :] = getattr(self, var_name)
+                        if var_name == "who_dies" and self.t_sim > 1:
+                            self.history[var_name][self.t_sim - 1, :] = getattr(
+                                self, var_name
+                            )
+                        else:
+                            self.history[var_name][self.t_sim, :] = getattr(
+                                self, var_name
+                            )
                 self.t_sim += 1
 
             return self.history

HARK/model.py

@@ -2,10 +2,28 @@
 Tools for crafting models.
 """
 
+from HARK.distribution import Distribution
+
+
+class Aggregate:
+    """
+    Used to designate a shock as an aggregate shock.
+    If so designated, draws from the shock will be scalar rather
+    than array valued.
+    """
+
+    def __init__(self, dist: Distribution):
+        self.dist = dist
+
 
 class Control:
     """
-    Should go in different model support module.
+    Used to designate a variabel that is a control variable.
+
+    Parameters
+    ----------
+    args : list of str
+        The labels of the variables that are in the information set of this control.
     """
 
     def __init__(self, args):

HARK/models/perfect_foresight.py

@@ -20,9 +20,9 @@
         "BoroCnstArt": None,
     },
     "dynamics": {
+        "y": lambda p: p,
         "m": lambda Rfree, a, y: Rfree * a + y,
         "c": Control(["m"]),
-        "y": lambda p: p,
         "p": lambda PermGroFac, p: PermGroFac * p,
         "a": lambda m, c: m - c,
     },

HARK/models/perfect_foresight_normalized.py

@@ -0,0 +1,31 @@
+from HARK.distribution import Bernoulli
+from HARK.model import Control
+
+# This way of distributing parameters across the scope is clunky
+# Can be handled better if parsed from a YAML file, probably
+# But it would be better to have a more graceful Python version as well.
+CRRA = (2.0,)
+LivPrb = 0.98
+
+model = {
+    "shocks": {
+        "live": Bernoulli(p=LivPrb),
+    },
+    "parameters": {
+        "DiscFac": 0.96,
+        "CRRA": CRRA,
+        "Rfree": 1.03,
+        "LivPrb": LivPrb,
+        "PermGroFac": 1.01,
+        "BoroCnstArt": None,
+    },
+    "dynamics": {
+        "p": lambda PermGroFac, p: PermGroFac * p,
+        "r_eff": lambda Rfree, PermGroFac: Rfree / PermGroFac,
+        "b_nrm": lambda r_eff, a_nrm: r_eff * a_nrm,
+        "m_nrm": lambda b_nrm: b_nrm + 1,
+        "c_nrm": Control(["m_nrm"]),
+        "a_nrm": lambda m_nrm, c_nrm: m_nrm - c_nrm,
+    },
+    "reward": {"u": lambda c: c ** (1 - CRRA) / (1 - CRRA)},
+}