-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathnelder_mead_LSC_nltax.py
executable file
·195 lines (131 loc) · 4.99 KB
/
nelder_mead_LSC_nltax.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
import sys
args = sys.argv
import numpy as np
import time
import subprocess
### use modified version of SCEConomy module
from SCEconomy_LSC_nltax import Economy, split_shock
import pickle
p_init = float(args[1])
rc_init = float(args[2])
num_core = args[3]
print('the code is running with ', num_core, 'cores...')
prices_init = [p_init, rc_init]
nd_log_file = './log/log.txt'
detailed_output_file = './log/detailed.txt'
f = open(detailed_output_file, 'w')
f.close()
dist_min = 10000000.0
econ_save = None
zgrid2 = np.load('./input_data/zgrid.npy') ** 2.0
prob = np.load('./DeBacker/prob_epsz.npy') #DeBacker
path_to_data_i_s = './tmp/data_i_s'
def curvedspace(begin, end, curve, num=100):
import numpy as np
ans = np.linspace(0, (end - begin)**(1.0/curve), num) ** (curve) + begin
ans[-1] = end #so that the last element is exactly end
return ans
agrid2 = curvedspace(0.0, 50.0, 2.0, 40)
alpha = 0.3 #new!
theta = 0.41
ynb_p_gdp = 0.25
xnb_p_gdp = 0.105
g_p_gdp = 0.13
pure_sweat_share = 0.10
yc_init = 1.04
GDP_implied = yc_init/(1. - ynb_p_gdp - pure_sweat_share/(1.-alpha))
ynb = ynb_p_gdp*GDP_implied
xnb = xnb_p_gdp*GDP_implied
g = g_p_gdp*GDP_implied
ome = 0.753033796603102
taup = 0.20
taub = np.array([0.137, 0.185, 0.202, 0.238, 0.266, 0.28]) * 0.50 #large one
psib = np.array([0.12837754, 0.14071072, 0.15, 0.20081269, 0.30081419, 0.37107904])
def target(prices):
global dist_min
global econ_save
p_ = prices[0]
rc_ = prices[1]
print('computing for the case p = {:f}, rc = {:f}'.format(p_, rc_), end = ', ')
###set any additional condition/parameters
econ = Economy(path_to_data_i_s = path_to_data_i_s, prob = prob, zgrid = zgrid2, agrid = agrid2,
g = g, yn = ynb, xnb = xnb, ome = ome, chi = 0.25,
scaling_n = GDP_implied, scaling_b = GDP_implied,
taub = taub, psib = psib, taup = taup,
alpha = alpha, theta = theta)
econ.set_prices(p = p_, rc = rc_)
with open('econ.pickle', mode='wb') as f: pickle.dump(econ, f)
#with open('econ.pickle', mode='rb') as f: econ = pickle.load(f)
t0 = time.time()
result = subprocess.run(['mpiexec', '-n', num_core, 'python', 'SCEconomy_LSC_nltax.py'], stdout=subprocess.PIPE)
t1 = time.time()
f = open(detailed_output_file, 'ab') #use byte mode
f.write(result.stdout)
f.close()
print('etime: {:f}'.format(t1 - t0), end = ', ')
time.sleep(1)
with open('econ.pickle', mode='rb') as f: econ = pickle.load(f)
w = econ.w
p = econ.p
rc = econ.rc
moms = econ.moms
# dist = np.sqrt(moms[0]**2.0 + moms[1]**2.0 + moms[2]**2.0)
dist = np.sqrt(moms[0]**2.0 + moms[1]**2.0)
if p != p_ or rc != rc_:
print('err: input prices and output prices do not coincide.')
print('p = ', p, ', p_ = ', p_)
print('rc = ', rc, ', rc_ = ', rc_)
# return
print('dist = {:f}'.format(dist))
f = open(nd_log_file, 'a')
f.writelines(str(p) + ', ' + str(rc) + ', ' + str(dist) + ', ' + str(moms[0]) + ', ' + str(moms[1]) + ', ' + str(moms[2]) + ', ' + str(moms[3]) + '\n')
f.close()
if dist < dist_min:
econ_save = econ
dist_min = dist
return dist
if __name__ == '__main__':
f = open(nd_log_file, 'w')
f.writelines('p, rc, dist, mom0, mom1, mom2, mom3\n')
f.writelines('yc_init = ' + str(yc_init) + '\n')
f.writelines('GDP_implied = ' + str(GDP_implied) + '\n')
f.close()
#load shocks
from markov import calc_trans, Stationary
num_pop = 100_000
sim_time = 3_000
data_i_s = np.ones((num_pop, sim_time), dtype = int)
#need to set initial state for zp
data_i_s[:, 0] = 7
np.random.seed(0)
data_rand = np.random.rand(num_pop, sim_time)
calc_trans(data_i_s, data_rand, prob)
data_i_s = data_i_s[:, 2000:]
np.save(path_to_data_i_s + '.npy' , data_i_s)
### 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('yc_init = ' + str(yc_init) + '\n')
# f.writelines('GDP_implied = ' + str(GDP_implied) + '\n')
f.close()
del data_i_s
split_shock(path_to_data_i_s, 100_000, int(num_core))
nm_result = None
from scipy.optimize import minimize
for i in range(5):
nm_result = minimize(target, prices_init, method='Nelder-Mead')
if nm_result.fun < 1.0e-3:
break
else:
prices_init = nm_result.x #restart
f = open(nd_log_file, 'a')
f.write(str(nm_result))
f.close()
###calculate other important variables###
econ = econ_save
with open('econ.pickle', mode='wb') as f: pickle.dump(econ, f)
#
#econ.calc_sweat_eq_value()
#econ.simulate_other_vars()
#econ.save_result()