Add parameter for minimum consumption amounts

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(

ogcore/TPI.py

@@ -129,7 +129,20 @@ def get_initial_SS_values(p):
 
 
 def firstdoughnutring(
-    guesses, r, w, p_tilde, bq, rm, tr, theta, factor, ubi, j, initial_b, p
+    guesses,
+    r,
+    w,
+    p_tilde,
+    p_i,
+    bq,
+    rm,
+    tr,
+    theta,
+    factor,
+    ubi,
+    j,
+    initial_b,
+    p,
 ):
     """
     Solves the first entries of the upper triangle of the twist doughnut. This
@@ -141,6 +154,7 @@ def firstdoughnutring(
         r (scalar): real interest rate
         w (scalar): real wage rate
         p_tilde (scalar): composite good price
+        p_i (Numpy array): output goods prices
         bq (scalar): bequest amounts by age
         rm (scalar): remittance amounts by age
         tr (scalar): government transfer amount
@@ -166,6 +180,7 @@ def firstdoughnutring(
         np.array([r]),
         np.array([w]),
         np.array([p_tilde]),
+        np.array([p_i]),
         b_s,
         np.array([b_splus1]),
         np.array([n]),
@@ -188,6 +203,7 @@ def firstdoughnutring(
         np.array([r]),
         np.array([w]),
         np.array([p_tilde]),
+        np.array([p_i]),
         b_s,
         b_splus1,
         np.array([n]),
@@ -219,6 +235,7 @@ def twist_doughnut(
     r,
     w,
     p_tilde,
+    p_i,
     bq,
     rm,
     tr,
@@ -244,6 +261,7 @@ def twist_doughnut(
         r (Numpy array): real interest rate
         w (Numpy array): real wage rate
         p_tilde (Numpy array): composite good price
+        p_i (Numpy array): output goods prices
         bq (Numpy array): bequest amounts by age, length s
         rm (Numpy array): remittance amounts by age, length s
         tr (Numpy array): government transfer amount
@@ -281,6 +299,7 @@ def twist_doughnut(
     w_s = w[t : t + length]
     r_s = r[t : t + length]
     p_tilde_s = p_tilde[t : t + length]
+    p_i_s = p_i[t : t + length, :]
     n_s = n_guess
     chi_n_s = np.diag(p.chi_n[t : t + p.S, :], max(p.S - length, 0))
     rho_s = np.diag(p.rho[t : t + p.S, :], max(p.S - length, 0))
@@ -289,6 +308,7 @@ def twist_doughnut(
         r_s,
         w_s,
         p_tilde_s,
+        p_i_s,
         b_s,
         b_splus1,
         n_s,
@@ -311,6 +331,7 @@ def twist_doughnut(
         r_s,
         w_s,
         p_tilde_s,
+        p_i_s,
         b_s,
         b_splus1,
         n_s,
@@ -408,6 +429,7 @@ def inner_loop(guesses, outer_loop_vars, initial_values, ubi, j, ind, p):
             r_p[0],
             w[0],
             p_tilde[0],
+            p_i[0, :],
             bq[0, -1, j],
             rm[0, -1, j],
             tr[0, -1, j],
@@ -463,6 +485,7 @@ def inner_loop(guesses, outer_loop_vars, initial_values, ubi, j, ind, p):
                 r_p,
                 w,
                 p_tilde,
+                p_i,
                 bq_to_use,
                 rm_to_use,
                 tr_to_use,
@@ -518,6 +541,7 @@ def inner_loop(guesses, outer_loop_vars, initial_values, ubi, j, ind, p):
                 r_p,
                 w,
                 p_tilde,
+                p_i,
                 bq_to_use,
                 rm_to_use,
                 tr_to_use,
@@ -916,26 +940,31 @@ def run_TPI(p, client=None):
         )
         r_p_path = utils.to_timepath_shape(r_p)
         p_tilde_path = utils.to_timepath_shape(p_tilde)
+
         wpath = utils.to_timepath_shape(w)
         c_mat = household.get_cons(
             r_p_path[: p.T, :, :],
             wpath[: p.T, :, :],
             p_tilde_path[: p.T, :, :],
+            p_i[: p.T, :],
             bmat_s,
             bmat_splus1,
             n_mat[: p.T, :, :],
             bqmat[: p.T, :, :],
             rmmat[: p.T, :, :],
             tax_mat,
             p.e,
+            p.tau_c[: p.T, :],
             p,
+            method="TPI",
         )
         c_i = household.get_ci(
             c_mat[: p.T, :, :],
             p_i[: p.T, :],
             p_tilde[: p.T],
             p.tau_c[: p.T, :],
             p.alpha_c,
+            p.c_min,
             "TPI",
         )
         sales_tax_mat = tax.cons_tax_liab(c_i, p_i, p, "TPI")
@@ -947,6 +976,7 @@ def run_TPI(p, client=None):
             p_tilde[: p.T],
             p.tau_c[: p.T, :],
             p.alpha_c,
+            p.c_min,
             "TPI",
         )
         y_before_tax_mat = household.get_y(

ogcore/default_parameters.json

@@ -404,6 +404,26 @@
             }
         }
     },
+    "c_min": {
+        "title": "Minimum consumption levels for each good in the composite consumption good",
+        "description": "Minimum consumption levels for each good in the composite consumption good.",
+        "section_1": "Household Parameters",
+        "section_2": "Behavioral Assumptions",
+        "notes": "Enter this value in model units.",
+        "type": "float",
+        "number_dims": 1,
+        "value": [
+            {
+                "value": [0.0]
+            }
+        ],
+        "validators": {
+            "range": {
+                "min": 0.0,
+                "max": 100.0
+            }
+        }
+    },
     "gamma": {
         "title": "Capital's share of output in firm production function",
         "description": "Capital's share of output in firm production function.",