def shrink_mat(array):
dim = 5
step = 2
sdist = Stationary(array)
ans = np.zeros((dim, dim))
for i in range(dim):
for j in range(dim):
ans[i,j] = sdist[step*i]*array[step*i, step*j:step*j+2].sum() + \
sdist[step*i+1]*array[step*i+1, step*j:step*j+2].sum()
ans[i, j] = ans[i, j] / sdist[step * i:step * i + 2].sum()
return ans
data_is_o = data_is_o[:, 2000:]
np.save(path_to_data_is_o + '.npy', data_is_o)
split_shock(path_to_data_is_o, 25_000, num_core)
del data_rand
### end generate shocks ###
### check
f = open(nd_log_file, 'w')
f.writelines(
np.array_str(np.bincount(data_i_s[:, 0]) /
np.sum(np.bincount(data_i_s[:, 0])),
precision=4,
suppress_small=True) + '\n')
f.writelines(
np.array_str(Stationary(prob), precision=4, suppress_small=True) +
'\n')
f.writelines(
np.array_str(np.bincount(data_is_o[:, 0]) /
np.sum(np.bincount(data_is_o[:, 0])),
precision=4,
suppress_small=True) + '\n')
f.writelines(
np.array_str(Stationary(prob_yo), precision=4, suppress_small=True) +
'\n')
# f.writelines('yc_init = ' + str(yc_init) + '\n')
# f.writelines('GDP_implied = ' + str(GDP_implied) + '\n')
f.close()
data_laborinc = w * data_eps * data_n
data_bizinc = p * data_ys - (rs + delk) * data_ks - data_x - w * data_ns
print('### labor income wepsn ###')
print('')
print('simulated result')
prob_wepsn = get_transition(data_laborinc[:, -2],
data_laborinc[:, -1],
num_bins=5,
full_output=True)[0]
print('transition of wepsn')
print(np.array_str(prob_wepsn, precision=4, suppress_small=True))
print('implied SS of wespn')
print(Stationary(prob_wepsn))
print('')
print('P_eps')
print(prob_eps)
print('SS of P_eps')
print(Stationary(prob_eps))
print('')
print('### biz income pys - (rs+delk)ks - wns - x ###')
print('')
print('simulated result')
prob_bizinc = get_transition(data_bizinc[:, -2],
data_bizinc[:, -1],
num_bins=5,