Merge pull request #1275 from alanlujan91/approx_population

Add arguments to discretize
econ-ark · Jun 19, 2024 · 13d94c8 · 13d94c8
2 parents 386a049 + 5c3136f
commit 13d94c8
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diff --git a/Documentation/CHANGELOG.md b/Documentation/CHANGELOG.md
@@ -8,6 +8,18 @@ For more information on HARK, see [our Github organization](https://github.com/e
 
 ## Changes
 
+### 0.15.2 (in development)
+
+Release Date: TBD
+
+#### Major Changes
+
+none
+
+#### Minor Changes
+
+- Fixes bug in `AgentPopulation` that caused discretization of distributions to not work. [1275](https://github.com/econ-ark/HARK/pull/1275)
+
 ### 0.15.1
 
 Release Date: June 15, 2024
@@ -86,13 +98,6 @@ Release Date: February 12, 2024
 - Adds option `sim_common_Rrisky` to control whether risky-asset models draw common or idiosyncratic returns in simulation. [#1250](https://github.com/econ-ark/HARK/pull/1250),[#1253](https://github.com/econ-ark/HARK/pull/1253)
 - Addresses [#1255](https://github.com/econ-ark/HARK/issues/1255). Makes age-varying stochastic returns possible and draws from their discretized version. [#1262](https://github.com/econ-ark/HARK/pull/1262)
 - Fixes bug in the metric that compares dictionaries with the same keys. [#1260](https://github.com/econ-ark/HARK/pull/1260)
-- 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)
-- Fixes bug in AgentType simulations where 'who_dies' for period t was being recorded in period t-1 in 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)
-- Fixes bug that default seed was being used in the initializing of income shock distributions. [1380](https://github.com/econ-ark/HARK/pull/1380)
 
 ### 0.13.0
 

diff --git a/HARK/core.py b/HARK/core.py
@@ -2005,11 +2005,11 @@ def approx_distributions(self, approx_params: dict):
         self.continuous_distributions = {}
         self.discrete_distributions = {}
 
-        for key, points in approx_params.items():
+        for key, args in approx_params.items():
             param = self.parameters[key]
             if key in self.distributed_params and isinstance(param, Distribution):
                 self.continuous_distributions[key] = param
-                self.discrete_distributions[key] = param.discretize(points)
+                self.discrete_distributions[key] = param.discretize(**args)
             else:
                 raise ValueError(
                     f"Warning: parameter {key} is not a Distribution found "

diff --git a/HARK/tests/test_core.py b/HARK/tests/test_core.py
@@ -156,7 +156,12 @@ def test_distributed_params(self):
         self.assertTrue("DiscFac" in self.agent_pop.distributed_params)
 
     def test_approx_agents(self):
-        self.agent_pop.approx_distributions({"CRRA": 3, "DiscFac": 4})
+        self.agent_pop.approx_distributions(
+            {
+                "CRRA": {"N": 3, "method": "equiprobable"},
+                "DiscFac": {"N": 4, "method": "equiprobable"},
+            }
+        )
 
         self.assertTrue("CRRA" in self.agent_pop.continuous_distributions)
         self.assertTrue("DiscFac" in self.agent_pop.continuous_distributions)
@@ -166,7 +171,12 @@ def test_approx_agents(self):
         self.assertEqual(self.agent_pop.agent_type_count, 12)
 
     def test_create_agents(self):
-        self.agent_pop.approx_distributions({"CRRA": 3, "DiscFac": 4})
+        self.agent_pop.approx_distributions(
+            {
+                "CRRA": {"N": 3, "method": "equiprobable"},
+                "DiscFac": {"N": 4, "method": "equiprobable"},
+            }
+        )
         self.agent_pop.create_distributed_agents()
 
         self.assertEqual(len(self.agent_pop.agents), 12)