adding YAML config for portfolio block and tests

econ-ark · Jul 3, 2024 · 2a09a27 · 2a09a27
1 parent 2db6527
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Showing 2 changed files with 20 additions and 32 deletions.
diff --git a/HARK/models/consumer.yaml b/HARK/models/consumer.yaml
@@ -31,30 +31,13 @@ blocks:
 
         reward:
             u: c ** (1 - CRRA) / (1 - CRRA)
-
-
-#portfolio_block = DBlock(
-#    **{
-#        "name": "portfolio",
-#        "shocks": {
-#            "risky_return": (Lognormal, {"mean": "Rfree + EqP", "std": "RiskyStd"})
-#        },
-#        "dynamics": {
-#            "stigma": Control(["a"]),
-#            "R": lambda stigma, Rfree, risky_return: Rfree
-#            + (risky_return - Rfree) * stigma,
-#        },
-#    }
-#)
-
-#tick_block = DBlock(
-#    **{
-#        "name": "tick",
-#        "dynamics": {
-#            "k": lambda a: a,
-#        },
-#    }
-#)
-
-#cons_problem = RBlock(blocks=[consumption_block, tick_block])
-#cons_portfolio_problem = RBlock(blocks=[consumption_block, portfolio_block, tick_block])
+    - &portfolio_choice
+        name: portfolio choice
+        shocks:
+            risky_return: !Lognormal
+                mean: Rfree + EqP
+                std: RiskyStd
+            dynamics:
+                stigma: !Control
+                    info: a
+                R: Rfree + (risky_return - Rfree) * stigma
diff --git a/HARK/tests/test_parser.py b/HARK/tests/test_parser.py
@@ -18,11 +18,16 @@ def test_parse(self):
 
         config = yaml.load(self.consumer_yaml_file, Loader=parser.harklang_loader())
 
-        self.assertEqual(config['calibration']['DiscFac'], 0.96)
-        self.assertEqual(config['blocks'][0]['name'], 'consumption normalized')
+        self.assertEqual(config["calibration"]["DiscFac"], 0.96)
+        self.assertEqual(config["blocks"][0]["name"], "consumption normalized")
 
-        ## construct and test the block
-        cons_norm_block = model.DBlock(**config['blocks'][0])
-        cons_norm_block.construct_shocks(config['calibration'])
+        ## construct and test the consumption block
+        cons_norm_block = model.DBlock(**config["blocks"][0])
+        cons_norm_block.construct_shocks(config["calibration"])
         cons_norm_block.discretize({"theta": {"N": 5}})
         self.assertEqual(cons_norm_block.calc_reward({"c": 1, "CRRA": 2})["u"], -1.0)
+
+        ## construct and test the portfolio block
+        portfolio_block = model.DBlock(**config["blocks"][1])
+        portfolio_block.construct_shocks(config["calibration"])
+        portfolio_block.discretize({"risky_return": {"N": 5}})