Add parameter for minimum consumption amounts

CHANGELOG.md

@@ -5,6 +5,12 @@ All notable changes to this project will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
+## [0.15.1] - 2026-01-19 12:00:00
+
+### Added
+
+- A new parameter `c_min` to the `Parameters` class that allows the user to specify minium consumption amounts by consumption good.  See PR [#1085](https://github.com/PSLmodels/OG-Core/pull/1085)
+
 ## [0.15.0] - 2025-12-03 12:00:00
 
 ### Added

docs/book/content/intro/parameters.md

@@ -43,6 +43,14 @@ _Valid Range:_ min = 0.0 and max = 1.0
 _Out-of-Range Action:_ error  
 
 
+####  `c_min`  
+_Description:_ Minimum consumption levels for each good in the composite consumption good.  
+_Notes:_ Enter this value in model units.  
+_Value Type:_ float  
+_Valid Range:_ min = 0.0 and max = 100.0  
+_Out-of-Range Action:_ error  
+
+
 ####  `chi_b`  
 _Description:_ Household utility weight on bequests.  
 _Value Type:_ float  
@@ -114,6 +122,7 @@ _Out-of-Range Action:_ error
 ####  `use_zeta`  
 _Description:_ Indicator variable for whether or not to use the zeta matrix to distribute bequests.  
 _Value Type:_ bool  
+_Valid Choices:_[True, False]  
 
 
 ### Model Dimensions
@@ -395,6 +404,12 @@ _Valid Range:_ min = 0.0 and max = 10.0
 _Out-of-Range Action:_ error  
 
 
+####  `baseline_theta`  
+_Description:_ Flag for use in reform simulations to keep Social Security system replacement rate constant between baseline and reform runs.  
+_Value Type:_ bool  
+_Valid Choices:_[True, False]  
+
+
 ### Spending
 
 ####  `alpha_T`  
@@ -586,21 +601,25 @@ _Out-of-Range Action:_ error
 ####  `constant_rates`  
 _Description:_ Flag to use linear tax functions.  
 _Value Type:_ bool  
+_Valid Choices:_[True, False]  
 
 
 ####  `zero_taxes`  
 _Description:_ Flag to run model without any individual income taxes.  
 _Value Type:_ bool  
+_Valid Choices:_[True, False]  
 
 
 ####  `analytical_mtrs`  
 _Description:_ Flag to use analytically derived marginal tax rates in tax functions.  
 _Value Type:_ bool  
+_Valid Choices:_[True, False]  
 
 
 ####  `age_specific`  
 _Description:_ Flag to use analytically derived marginal tax rates in tax functions.  
 _Value Type:_ bool  
+_Valid Choices:_[True, False]  
 
 
 ####  `tax_func_type`  
@@ -730,6 +749,7 @@ _Out-of-Range Action:_ error
 _Description:_ Use constant demographics.  
 _Notes:_ This boolean allows one to use empirical mortality rates, but keep the population distribution constant across periods.  Note that immigration is shut off when this is true.  
 _Value Type:_ bool  
+_Valid Choices:_[True, False]  
 
 
 ####  `omega`  
@@ -842,6 +862,7 @@ _Out-of-Range Action:_ error
 ####  `reform_use_baseline_solution`  
 _Description:_ Whether or not the baseline SS solution is used for starting values when solving the reform.  
 _Value Type:_ bool  
+_Valid Choices:_[True, False]  
 
 
 ####  `initial_guess_r_SS`  

docs/make_params.py

@@ -5,7 +5,6 @@
 import os
 import sys
 
-
 CURDIR_PATH = os.path.abspath(os.path.dirname(__file__))
 OGCORE_PATH = os.path.join(CURDIR_PATH, "..", "ogcore")
 TEMPLATE_PATH = os.path.join(CURDIR_PATH, "templates")

docs/make_vars.py

@@ -5,7 +5,6 @@
 import os
 import sys
 
-
 CURDIR_PATH = os.path.abspath(os.path.dirname(__file__))
 OGCORE_PATH = os.path.join(CURDIR_PATH, "..", "ogcore")
 TEMPLATE_PATH = os.path.join(CURDIR_PATH, "templates")

examples/multi_industry_example.py

@@ -19,7 +19,6 @@
 import matplotlib.pyplot as plt
 import ogcore
 
-
 ogcore.TPI.ENFORCE_SOLUTION_CHECKS = False
 
 # Use a custom matplotlib style file for plots

ogcore/SS.py

@@ -39,7 +39,7 @@ def euler_equation_solver(guesses, *args):
 
     Args:
         guesses (Numpy array): initial guesses for b and n, length 2S
-        args (tuple): tuple of arguments (r, w, p_tilde, bq, TR, factor, j, p)
+        args (tuple): tuple of arguments (r, w, p_tilde, p_i, bq, TR, factor, j, p)
         r (scalar): real interest rate
         w (scalar): real wage rate
         p_tilde (scalar): composite good price
@@ -54,7 +54,7 @@ def euler_equation_solver(guesses, *args):
         errros (Numpy array): errors from FOCs, length 2S
 
     """
-    (r, w, p_tilde, bq, rm, tr, ubi, factor, j, p) = args
+    r, w, p_tilde, p_i, bq, rm, tr, ubi, factor, j, p = args
 
     b_guess = np.array(guesses[: p.S])
     n_guess = np.array(guesses[p.S :])
@@ -67,6 +67,7 @@ def euler_equation_solver(guesses, *args):
         r,
         w,
         p_tilde,
+        p_i,
         b_s,
         b_splus1,
         n_guess,
@@ -88,6 +89,7 @@ def euler_equation_solver(guesses, *args):
         r,
         w,
         p_tilde,
+        p_i,
         b_s,
         b_splus1,
         n_guess,
@@ -143,13 +145,15 @@ def euler_equation_solver(guesses, *args):
         r,
         w,
         p_tilde,
+        p_i,
         b_s,
         b_splus1,
         n_guess,
         bq,
         rm,
         taxes,
         p.e[-1, :, j],
+        p.tau_c[-1, :],
         p,
     )
     mask6 = cons < 0
@@ -164,6 +168,7 @@ def solve_for_j(
     r_p,
     w,
     p_tilde,
+    p_i,
     bq_j,
     rm_j,
     tr_j,
@@ -180,6 +185,7 @@ def solve_for_j(
         r_p (scalar): return on household investment portfolio
         w (scalar): real wage rate
         p_tilde (scalar): composite good price
+        p_i (Numpy array): prices for consumption good i
         bq_j (Numpy array): bequest amounts by age, length S
         rm_j (Numpy array): remittance amounts by age, length S
         tr_j (Numpy array): government transfer amount by age, length S
@@ -200,6 +206,7 @@ def solve_for_j(
             r_p,
             w,
             p_tilde,
+            p_i,
             bq_j,
             rm_j,
             tr_j,
@@ -314,6 +321,7 @@ def inner_loop(outer_loop_vars, p, client):
                 r_p,
                 w,
                 p_tilde,
+                p_i,
                 bq[:, j],
                 rm[:, j],
                 tr[:, j],
@@ -346,6 +354,7 @@ def inner_loop(outer_loop_vars, p, client):
                     r_p,
                     w,
                     p_tilde,
+                    p_i,
                     bq[:, j],
                     rm[:, j],
                     tr[:, j],
@@ -365,6 +374,7 @@ def inner_loop(outer_loop_vars, p, client):
                 r_p,
                 w,
                 p_tilde,
+                p_i,
                 bq[:, j],
                 rm[:, j],
                 tr[:, j],
@@ -416,16 +426,20 @@ def inner_loop(outer_loop_vars, p, client):
         r_p,
         w,
         p_tilde,
+        p_i,
         b_s,
         b_splus1,
         nssmat,
         bq,
         rm,
         net_tax,
         np.squeeze(p.e[-1, :, :]),
+        p.tau_c[-1, :],
         p,
     )
-    c_i = household.get_ci(c_s, p_i, p_tilde, p.tau_c[-1, :], p.alpha_c)
+    c_i = household.get_ci(
+        c_s, p_i, p_tilde, p.tau_c[-1, :], p.alpha_c, p.c_min
+    )
     L = aggr.get_L(nssmat, p, "SS")
     B = aggr.get_B(bssmat, p, "SS", False)
 
@@ -551,13 +565,15 @@ def inner_loop(outer_loop_vars, p, client):
         new_r_p,
         new_w,
         new_p_tilde,
+        new_p_i,
         b_s,
         bssmat,
         nssmat,
         new_bq,
         new_rm,
         taxss,
         np.squeeze(p.e[-1, :, :]),
+        p.tau_c[-1, :],
         p,
     )
     (
@@ -990,13 +1006,15 @@ def SS_solver(
         r_p_ss,
         wss,
         p_tilde_ss,
+        p_i_ss,
         bssmat_s,
         bssmat_splus1,
         nssmat,
         bqssmat,
         rmssmat,
         taxss,
         np.squeeze(p.e[-1, :, :]),
+        p.tau_c[-1, :],
         p,
     )
     c_i = household.get_ci(
@@ -1005,6 +1023,7 @@ def SS_solver(
         p_tilde_ss,
         p.tau_c[-1, :],
         p.alpha_c,
+        p.c_min,
         "SS",
     )
     sales_tax_ss = tax.cons_tax_liab(c_i, p_i_ss, p, "SS")
@@ -1013,7 +1032,7 @@ def SS_solver(
     )
     Css = aggr.get_C(cssmat, p, "SS")
     c_i_ss_mat = household.get_ci(
-        cssmat, p_i_ss, p_tilde_ss, p.tau_c[-1, :], p.alpha_c
+        cssmat, p_i_ss, p_tilde_ss, p.tau_c[-1, :], p.alpha_c, p.c_min
     )
     C_vec_ss = np.zeros(p.I)
     for i_ind in range(
@@ -1235,7 +1254,7 @@ def SS_fsolve(guesses, *args):
             implied outer loop variables
 
     """
-    (bssmat, nssmat, TR_ss, Ig_baseline, factor_ss, p, client) = args
+    bssmat, nssmat, TR_ss, Ig_baseline, factor_ss, p, client = args
 
     # Rename the inputs
     r_p = guesses[0]