def run_simulation(Gamma0,Eps,ineq_slope=2/.9,tfp_level=1.,chi=0):
Gamma,Z,Y,Shocks,y = {},{},{},{},{}
Para.ineq_slope=ineq_slope
Para.chi=chi
Para.tfp_level=tfp_level
approximate.calibrate(Para)
simulate.approximate = approximate
T=len(Eps)
Gamma[0] = Gamma0
Z[0] = 0.
simulate.simulate_aggstate(Para,Gamma,Z,Y,Shocks,y,T-1,agg_shocks= Eps)
return Gamma,Z,Y,Shocks,y
def run_simulation(
ex, Gamma0,
Eps): ## function to run all cases with MPI and store the simulation
Para.chi = Case[ex][0]
conditional_mean_flag = Case[ex][1]
inequlity_shock_flag = Case[ex][2]
Gamma, Z, Y, Shocks, y = {}, {}, {}, {}, {}
T = len(Eps)
Gamma[0] = Gamma0
Z[0] = 0.
if inequlity_shock_flag == 0:
Para.ineq_slope = 0
else:
Para.ineq_slope = 1.0 / 0.8
approximate.calibrate(Para)
simulate.approximate = approximate
if conditional_mean_flag == 1:
simulate.simulate_aggstate_ConditionalMean(Para,
Gamma,
Z,
Y,
Shocks,
y,
T - 1,
agg_shocks=Eps)
else:
simulate.simulate_aggstate(Para,
Gamma,
Z,
Y,
Shocks,
y,
T - 1,
agg_shocks=Eps)
data_simulation = Gamma, Z, Y, Shocks, y, Case[ex]
with open('data_simulation_new' + str(ex) + '.pickle', 'wb') as f:
pickle.dump(data_simulation, f)
Para.gamma = gamma
Para.Gov = gov
Para.chi = -0.06
N = len(thetas)
T = 200
debt = []
alphalist = []
for alpha in np.linspace(.03, .16, 15):
Gamma, Z, Y, Shocks, y = {}, {}, {}, {}, {}
Gamma[0] = Gamma0
Gamma[0][:, 3] = np.linspace(0.2 - alpha, 0.2 + alpha, N)
Z[0] = 0.
Para.sigma_E = 0.25 #allows for quicker convergence
approximate.calibrate(Para)
simulate.approximate = approximate
Eps = np.random.randn(T)
Eps = np.minimum(3., np.maximum(-3., Eps))
simulate.simulate_aggstate_ConditionalMean(Para,
Gamma,
Z,
Y,
Shocks,
y,
T - 1,
agg_shocks=Eps)
Para.sigma_E = 0.025
approximate.calibrate(Para)
approx = approximate.approximate(Gamma[T - 2])
data = {}