adding python model configuration for normalized perfect foresight; g…

…eneric monte carlo example shows exact match

HARK/models/perfect_foresight_normalized.py

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+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)
+    }
+}