Rename phelps_deaton -> deterministic

tests/test_analytical_solution.py

@@ -6,7 +6,7 @@
 from lcm.entry_point import get_lcm_function
 from numpy.testing import assert_array_almost_equal as aaae
 
-from tests.test_models.phelps_deaton import PHELPS_DEATON_WITH_FILTERS
+from tests.test_models.deterministic import BASE_MODEL_WITH_FILTERS
 
 # ======================================================================================
 # Model specifications
@@ -23,11 +23,11 @@
 
 TEST_CASES = {
     "iskhakov_2017_five_periods": {
-        "model": {**PHELPS_DEATON_WITH_FILTERS, "n_periods": 5},
+        "model": {**BASE_MODEL_WITH_FILTERS, "n_periods": 5},
         "params": {**ISKHAVOV_2017_PARAMS, "utility": {"disutility_of_work": 1.0}},
     },
     "iskhakov_2017_low_delta": {
-        "model": {**PHELPS_DEATON_WITH_FILTERS, "n_periods": 3},
+        "model": {**BASE_MODEL_WITH_FILTERS, "n_periods": 3},
         "params": {**ISKHAVOV_2017_PARAMS, "utility": {"disutility_of_work": 0.1}},
     },
 }

tests/test_entry_point.py

@@ -10,17 +10,17 @@
 from lcm.state_space import create_state_choice_space
 from pybaum import tree_equal, tree_map
 
-from tests.test_models.phelps_deaton import (
-    PHELPS_DEATON,
-    PHELPS_DEATON_FULLY_DISCRETE,
-    PHELPS_DEATON_WITH_FILTERS,
+from tests.test_models.deterministic import (
+    BASE_MODEL,
+    BASE_MODEL_FULLY_DISCRETE,
+    BASE_MODEL_WITH_FILTERS,
     utility,
 )
 
 MODELS = {
-    "simple": PHELPS_DEATON,
-    "with_filters": PHELPS_DEATON_WITH_FILTERS,
-    "fully_discrete": PHELPS_DEATON_FULLY_DISCRETE,
+    "simple": BASE_MODEL,
+    "with_filters": BASE_MODEL_WITH_FILTERS,
+    "fully_discrete": BASE_MODEL_FULLY_DISCRETE,
 }
 
 
@@ -45,7 +45,7 @@ def test_get_lcm_function_with_solve_target(user_model):
 
 @pytest.mark.parametrize(
     "user_model",
-    [PHELPS_DEATON, PHELPS_DEATON_FULLY_DISCRETE],
+    [BASE_MODEL, BASE_MODEL_FULLY_DISCRETE],
     ids=["simple", "fully_discrete"],
 )
 def test_get_lcm_function_with_simulation_target_simple(user_model):
@@ -66,7 +66,7 @@ def test_get_lcm_function_with_simulation_target_simple(user_model):
 
 @pytest.mark.parametrize(
     "user_model",
-    [PHELPS_DEATON, PHELPS_DEATON_FULLY_DISCRETE],
+    [BASE_MODEL, BASE_MODEL_FULLY_DISCRETE],
     ids=["simple", "fully_discrete"],
 )
 def test_get_lcm_function_with_simulation_is_coherent(user_model):
@@ -109,7 +109,7 @@ def test_get_lcm_function_with_simulation_is_coherent(user_model):
 
 @pytest.mark.parametrize(
     "user_model",
-    [PHELPS_DEATON_WITH_FILTERS],
+    [BASE_MODEL_WITH_FILTERS],
     ids=["with_filters"],
 )
 def test_get_lcm_function_with_simulation_target_with_filters(user_model):
@@ -137,7 +137,7 @@ def test_get_lcm_function_with_simulation_target_with_filters(user_model):
 
 
 def test_create_compute_conditional_continuation_value():
-    model = process_model(PHELPS_DEATON)
+    model = process_model(BASE_MODEL)
 
     params = {
         "beta": 1.0,
@@ -180,7 +180,7 @@ def test_create_compute_conditional_continuation_value():
 
 
 def test_create_compute_conditional_continuation_value_with_discrete_model():
-    model = process_model(PHELPS_DEATON_FULLY_DISCRETE)
+    model = process_model(BASE_MODEL_FULLY_DISCRETE)
 
     params = {
         "beta": 1.0,
@@ -228,7 +228,7 @@ def test_create_compute_conditional_continuation_value_with_discrete_model():
 
 
 def test_create_compute_conditional_continuation_policy():
-    model = process_model(PHELPS_DEATON)
+    model = process_model(BASE_MODEL)
 
     params = {
         "beta": 1.0,
@@ -272,7 +272,7 @@ def test_create_compute_conditional_continuation_policy():
 
 
 def test_create_compute_conditional_continuation_policy_with_discrete_model():
-    model = process_model(PHELPS_DEATON_FULLY_DISCRETE)
+    model = process_model(BASE_MODEL_FULLY_DISCRETE)
 
     params = {
         "beta": 1.0,

tests/test_model_functions.py

@@ -10,7 +10,7 @@
 from lcm.state_space import create_state_choice_space
 from numpy.testing import assert_array_equal
 
-from tests.test_models.phelps_deaton import PHELPS_DEATON, utility
+from tests.test_models.deterministic import BASE_MODEL, utility
 
 
 def test_get_combined_constraint():
@@ -42,7 +42,7 @@ def h():
 
 
 def test_get_utility_and_feasibility_function():
-    model = process_model(PHELPS_DEATON)
+    model = process_model(BASE_MODEL)
 
     params = {
         "beta": 1.0,

tests/test_models/phelps_deaton.py → tests/test_models/deterministic.py

@@ -1,6 +1,6 @@
-"""Example specifications of the Phelps-Deaton model.
+"""Example specifications of a deterministic consumption-saving model.
 
-This specification extends the example model presented in the paper: "The endogenous
+This specification builds on the example model presented in the paper: "The endogenous
 grid method for discrete-continuous dynamic choice models with (or without) taste
 shocks" by Fedor Iskhakov, Thomas H. Jørgensen, John Rust and Bertel Schjerning (2017,
 https://doi.org/10.3982/QE643).
@@ -84,10 +84,10 @@ def absorbing_retirement_filter(retirement, lagged_retirement):
 
 
 # ======================================================================================
-# Model specification and parameters
+# Model specifications
 # ======================================================================================
 
-PHELPS_DEATON = {
+BASE_MODEL = {
     "functions": {
         "utility": utility,
         "next_wealth": next_wealth,
@@ -117,7 +117,7 @@ def absorbing_retirement_filter(retirement, lagged_retirement):
 }
 
 
-PHELPS_DEATON_FULLY_DISCRETE = {
+BASE_MODEL_FULLY_DISCRETE = {
     "functions": {
         "utility": utility,
         "next_wealth": next_wealth,
@@ -140,7 +140,7 @@ def absorbing_retirement_filter(retirement, lagged_retirement):
 }
 
 
-PHELPS_DEATON_WITH_FILTERS = {
+BASE_MODEL_WITH_FILTERS = {
     "functions": {
         "utility": utility_with_filter,
         "next_wealth": next_wealth,

tests/test_models/stochastic.py

@@ -1,11 +1,11 @@
-"""Example specifications of a simple Phelps-Deaton style stochastic model.
+"""Example specifications of a stochastic consumption-saving model.
 
 This specification is motivated by the example model presented in the paper: "The
 endogenous grid method for discrete-continuous dynamic choice models with (or without)
 taste shocks" by Fedor Iskhakov, Thomas H. Jørgensen, John Rust and Bertel Schjerning
 (2017, https://doi.org/10.3982/QE643).
 
-See also the specifications in tests/test_models/phelps_deaton.py.
+See also the specifications in tests/test_models/deterministic.py.
 
 """
 

tests/test_next_state.py

@@ -5,15 +5,15 @@
 from lcm.process_model import process_model
 from pybaum import tree_equal
 
-from tests.test_models.phelps_deaton import PHELPS_DEATON
+from tests.test_models.deterministic import BASE_MODEL
 
 # ======================================================================================
 # Solve target
 # ======================================================================================
 
 
 def test_get_next_state_function_with_solve_target():
-    model = process_model(PHELPS_DEATON)
+    model = process_model(BASE_MODEL)
     got_func = get_next_state_function(model, target="solve")
 
     params = {

tests/test_process_model.py

@@ -17,10 +17,10 @@
 from numpy.testing import assert_array_equal
 from pandas.testing import assert_frame_equal
 
-from tests.test_models.phelps_deaton import (
+from tests.test_models.deterministic import (
+    BASE_MODEL,
+    BASE_MODEL_WITH_FILTERS,
     N_GRID_POINTS,
-    PHELPS_DEATON,
-    PHELPS_DEATON_WITH_FILTERS,
 )
 
 
@@ -100,8 +100,8 @@ def test_get_grids(user_model):
     assert_array_equal(got["c"], jnp.array([2, 3]))
 
 
-def test_process_phelps_deaton_with_filters():
-    model = process_model(PHELPS_DEATON_WITH_FILTERS)
+def test_process_model_with_filters():
+    model = process_model(BASE_MODEL_WITH_FILTERS)
 
     # Variable Info
     assert (
@@ -156,8 +156,8 @@ def test_process_phelps_deaton_with_filters():
     assert ~model.function_info.loc["utility"].to_numpy().any()
 
 
-def test_process_phelps_deaton():
-    model = process_model(PHELPS_DEATON)
+def test_process_model():
+    model = process_model(BASE_MODEL)
 
     # Variable Info
     assert ~(model.variable_info["is_sparse"].to_numpy()).any()

tests/test_regression_test.py

@@ -5,9 +5,9 @@
 from numpy.testing import assert_array_almost_equal as aaae
 from pandas.testing import assert_frame_equal
 
-from tests.test_models.phelps_deaton import PHELPS_DEATON
+from tests.test_models.deterministic import BASE_MODEL
 
-REGRESSION_TEST_MODEL = {**PHELPS_DEATON, "n_perids": 5}
+REGRESSION_TEST_MODEL = {**BASE_MODEL, "n_perids": 5}
 REGRESSION_TEST_PARAMS = {
     "beta": 0.95,
     "utility": {"disutility_of_work": 1.0},

tests/test_simulate.py

@@ -27,10 +27,10 @@
 from numpy.testing import assert_array_almost_equal, assert_array_equal
 from pybaum import tree_equal
 
-from tests.test_models.phelps_deaton import (
+from tests.test_models.deterministic import (
+    BASE_MODEL,
+    BASE_MODEL_WITH_FILTERS,
     N_GRID_POINTS,
-    PHELPS_DEATON,
-    PHELPS_DEATON_WITH_FILTERS,
 )
 
 # ======================================================================================
@@ -40,7 +40,7 @@
 
 @pytest.fixture()
 def simulate_inputs():
-    user_model = {**PHELPS_DEATON, "n_periods": 1}
+    user_model = {**BASE_MODEL, "n_periods": 1}
     model = process_model(user_model)
 
     _, space_info, _, _ = create_state_choice_space(
@@ -104,9 +104,9 @@ def test_simulate_using_raw_inputs(simulate_inputs):
 
 
 @pytest.fixture()
-def phelps_deaton_model_solution():
+def base_model_solution():
     def _model_solution(n_periods):
-        model = {**PHELPS_DEATON, "n_periods": n_periods}
+        model = {**BASE_MODEL, "n_periods": n_periods}
         model["functions"] = {
             # remove dependency on age, so that wage becomes a parameter
             name: func
@@ -130,9 +130,9 @@ def _model_solution(n_periods):
     return _model_solution
 
 
-@pytest.mark.parametrize("n_periods", range(3, PHELPS_DEATON["n_periods"] + 1))
-def test_simulate_using_get_lcm_function(phelps_deaton_model_solution, n_periods):
-    vf_arr_list, params, model = phelps_deaton_model_solution(n_periods)
+@pytest.mark.parametrize("n_periods", range(3, BASE_MODEL["n_periods"] + 1))
+def test_simulate_using_get_lcm_function(base_model_solution, n_periods):
+    vf_arr_list, params, model = base_model_solution(n_periods)
 
     simulate_model, _ = get_lcm_function(model=model, targets="simulate")
 
@@ -175,7 +175,7 @@ def test_simulate_using_get_lcm_function(phelps_deaton_model_solution, n_periods
 
 
 def test_effect_of_beta_on_last_period():
-    model = {**PHELPS_DEATON, "n_periods": 5}
+    model = {**BASE_MODEL, "n_periods": 5}
 
     # Model solutions
     # ==================================================================================
@@ -229,7 +229,7 @@ def test_effect_of_beta_on_last_period():
 
 
 def test_effect_of_disutility_of_work():
-    model = {**PHELPS_DEATON, "n_periods": 5}
+    model = {**BASE_MODEL, "n_periods": 5}
 
     # Model solutions
     # ==================================================================================
@@ -404,7 +404,7 @@ def test_filter_ccv_policy():
 
 
 def test_create_data_state_choice_space():
-    model = process_model(PHELPS_DEATON_WITH_FILTERS)
+    model = process_model(BASE_MODEL_WITH_FILTERS)
     got_space, got_segment_info = create_data_scs(
         states={
             "wealth": jnp.array([10.0, 20.0]),

tests/test_state_space.py

@@ -13,11 +13,11 @@
 )
 from numpy.testing import assert_array_almost_equal as aaae
 
-from tests.test_models.phelps_deaton import PHELPS_DEATON_WITH_FILTERS
+from tests.test_models.deterministic import BASE_MODEL_WITH_FILTERS
 
 
 def test_create_state_choice_space():
-    _model = process_model(PHELPS_DEATON_WITH_FILTERS)
+    _model = process_model(BASE_MODEL_WITH_FILTERS)
     create_state_choice_space(
         model=_model,
         period=0,