perfect foresight model data

Documentation/CHANGELOG.md

@@ -25,6 +25,7 @@ Release Date: TBD
 - Fixes bug in the calc_jacobian method. [#1342](https://github.com/econ-ark/HARK/pull/1342)
 - 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)
 
 ### 0.13.0
 

HARK/model.py

@@ -0,0 +1,11 @@
+"""
+Tools for crafting models.
+"""
+
+class Control:
+    """
+    Should go in different model support module.
+    """
+
+    def __init__(self, args):
+        pass

HARK/models/fisher.py

@@ -0,0 +1,30 @@
+"""
+A model file for a Fisher 2-period consumption problem.
+"""
+
+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,
+
+model = {
+    'shocks' : {},
+    'parameters' : {
+        'DiscFac' : 0.96,
+        'CRRA' : CRRA,
+        'Rfree' : 1.03,
+        'y' : [1.0, 1.0],
+        'BoroCnstArt' : None,
+    },
+    'dynamics' : {
+        'm' : lambda Rfree, a, y : Rfree * a + y,
+        'c' : Control(['m']),
+        'a' : lambda m, c : m - c
+    },
+    'reward' : {
+        'u' : lambda c : c ** (1 - CRRA) / (1 - CRRA)
+    }
+}

HARK/models/perfect_foresight.py

@@ -0,0 +1,32 @@
+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' : {
+        '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
+    },
+    'reward' : {
+        'u' : lambda c : c ** (1 - CRRA) / (1 - CRRA)
+    }
+}