CES to SS

Python/ogusa/SS.py

@@ -336,7 +336,7 @@ def inner_loop(outer_loop_vars, params, baseline):
         else:
             K = B - debt_ratio_ss*Y
     else:
-        K_params = (alpha, delta, Z)
+        K_params = (Z, gamma, epsilon, delta)
         K = firm.get_K(L, ss_firm_r, K_params)
     # Y_params = (alpha, Z)
     Y_params = (Z, gamma, epsilon)
@@ -542,7 +542,7 @@ def SS_solver(b_guess_init, n_guess_init, rss, wss, T_Hss, factor_ss, params, ba
         Iss = firm.get_I(bssmat_splus1, Kss, Kss, Iss_params)
     else:
         # Compute capital (K) and wealth (B) separately
-        Kss_params = (alpha, delta, Z)
+        Kss_params = (Z, gamma, epsilon, delta)
         Kss = firm.get_K(Lss, ss_firm_r, Kss_params)
         Iss_params = (delta, g_y, omega_SS, lambdas, imm_rates, g_n_ss, 'SS')
         InvestmentPlaceholder = np.zeros(bssmat_splus1.shape)
@@ -735,7 +735,7 @@ def SS_fsolve(guesses, params):
 
 
     # Check and punish violations
-    if r <= 0:
+    if r+delta <= 0:
         error1 = 1e9
     #if r > 1:
     #    error1 += 1e9

Python/ogusa/firm.py

@@ -44,8 +44,7 @@ def get_r(Y, K, params):
         r = gamma - delta
     else:
         r = ((Z**((epsilon-1)/epsilon))*(((gamma*Y)/K)**(1/epsilon))) - delta
-    print 'r, r+delta = ', r, r+delta
-
+
     return r
 
 
@@ -198,7 +197,16 @@ def get_K(L, r, params):
     Returns: r
     '''
 
-    alpha, delta , Z = params
+    Z, gamma, epsilon, delta = params
+    print 'USING firm.getK()'
+    if epsilon == 1:
+        K = (gamma*Z/(r+delta))**(1/(1-gamma)) * L
+    elif epsilon == 0:
+        K = (1-((1-gamma)*L))/gamma
+    else:
+        K = (((1-gamma)**(1/(epsilon-1)))*(((((r+delta)**(epsilon-1))*(gamma**((1-epsilon)/epsilon))
+             *(Z**(1-epsilon)))-(gamma**(1/epsilon)))**(epsilon/(1-epsilon)))*L)
+
     print 'USING firm.getK()'
-    K = (alpha*Z/(r+delta))**(1/(1-alpha)) * L
+
     return K

Python/ogusa/parameters.py

@@ -309,9 +309,9 @@ def get_parameters(test=False, baseline=False, guid='', user_modifiable=False, m
     sigma = 1.5 # value from Attanasio, Banks, Meghir and Weber (JEBS, 1999)
     alpha = .35 # many use 0.33, but many find that capitals share is increasing (e.g. Elsby, Hobijn, and Sahin (BPEA, 2013))
     gamma = 0.35
-    epsilon = 0.9#0.6
+    epsilon = 0.6
     Z = 1.0
-    delta_annual = 0.02#.05 # approximately the value from Kehoe calibration exercise: http://www.econ.umn.edu/~tkehoe/classes/calibration-04.pdf
+    delta_annual = 0.05 # approximately the value from Kehoe calibration exercise: http://www.econ.umn.edu/~tkehoe/classes/calibration-04.pdf
     delta = 1 - ((1 - delta_annual) ** (float(ending_age - starting_age) / S))
     ltilde = 1.0
     g_y_annual = 0.03
@@ -460,15 +460,15 @@ def get_parameters(test=False, baseline=False, guid='', user_modifiable=False, m
 
 
     ## To shut off demographics, uncomment the following 9 lines of code
-    g_n_ss = 0.0
-    surv_rate1 = np.ones((S,))# prob start at age S
-    surv_rate1[1:] = np.cumprod(surv_rate[:-1], dtype=float)
-    omega_SS = np.ones(S)*surv_rate1# number of each age alive at any time
-    omega_SS = omega_SS/omega_SS.sum()
-    imm_rates = np.zeros((T+S,S))
-    omega = np.tile(np.reshape(omega_SS,(1,S)),(T+S,1))
-    omega_S_preTP = omega_SS
-    g_n_vector = np.tile(g_n_ss,(T+S,))
+    # g_n_ss = 0.0
+    # surv_rate1 = np.ones((S,))# prob start at age S
+    # surv_rate1[1:] = np.cumprod(surv_rate[:-1], dtype=float)
+    # omega_SS = np.ones(S)*surv_rate1# number of each age alive at any time
+    # omega_SS = omega_SS/omega_SS.sum()
+    # imm_rates = np.zeros((T+S,S))
+    # omega = np.tile(np.reshape(omega_SS,(1,S)),(T+S,1))
+    # omega_S_preTP = omega_SS
+    # g_n_vector = np.tile(g_n_ss,(T+S,))
 
     e = inc.get_e_interp(S, omega_SS, omega_SS_80, lambdas, plot=False)
     # e_hetero = get_e(S, J, starting_age, ending_age, lambdas, omega_SS, flag_graphs)