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markov.py
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markov.py
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#########################################################
# This code is for calculation of markov_autoregression
# 2017/01/16 S.H. Cho
# Email : thecho7@gist.ac.kr
#########################################################
#%matplotlib inline
####### Import dependencies #####
import numpy as np
import pandas as pd
import statsmodels.api as sm
import matplotlib.pyplot as plt
import seaborn as sn
from decimal import Decimal
# NBER recessions
from pandas_datareader.data import DataReader
from datetime import datetime
usrec = DataReader('USREC', 'fred', start=datetime(1947, 1, 1), end=datetime(2013, 4, 1))
import csv
######## Load data #####
temp = open('1_r.csv', 'r') # 1_r.csv is a file with raw dataset
temp2 = open('result1.csv', 'a') # result1.csv is a resulting file will be created.
temp3 = open('2_r.csv', 'r') # 1_r.csv is a file with raw dataset
raw = csv.reader(temp)
raw2 = csv.reader(temp3)
####### Calculate the number of companies #####
# matrix size is the same as the number of company
company = 1004 # First index in the list of companies (1004)
compidx = company
i = 0
yr = 0
comp_yr_match = []
compid = [] # Store the companys' ID in an array
compyr = [] # Store the companys' year series in an array
row = 0
compid.append('1004')
for row in raw:
compyr.append(row[1])
if row[0] == str(compidx):
yr = yr + 1
continue
else:
comp_yr_match.append(yr)
i = i + 1
compidx = row[0]
compid.append(row[0])
yr = 1
comp_yr_match.append(yr)
i = i + 1
#print(compyr)
#print(comp_yr_match)
gnpmat = np.zeros((i, 21)) # gnpmat is a matrix with size i by 21. i is the number of companies and 21 is the maximum length of the annual data.
j = 0
k = 0
####### Append the data in the matrix (gnpmat). In every case, row[14] indicates 'c_compen'
for row in raw2:
if row[0] == str(company):
if row[14]:
gnpmat[k][j] = row[14]
j = j + 1
else:
continue
else:
company = row[0]
j = 0
k = k + 1
i=0
j=0
k=0
l=0
#print(len(gnpmat))
#print(len(gnpmat[1,:]))
#print(len(comp_yr_match))
####### By utilizing the data in gnpmat, calculate filtered marginal probabilities. After calculation, complete the file we want.
####### Consider only the data has length over 10. The resulting csv file has float numbers, inf (infinite), NaN (Not a number) and 0.
####### If you want, you can use res_hamilton.smoothed_marginal_probabilities[0] instead of res_hamilton.filtered_marginal_probabilities[0]
####### Clearly, you can utilize the result with (res_hamilton.filtered_marginal_probabilities[0], 1 - res_hamilton.filtered_marginal_probabilities[0])
####### res_hamilton.filtered_marginal_probabilities[0] + (1 - res_hamilton.filtered_marginal_probabilities[0]) = 1
for i in range(0, len(gnpmat)):
compgnp = gnpmat[i]
print i
final = compgnp[~(compgnp==0)]
print final
if len(final) == 0:
#dta_hamilton = pd.Series(final, index=pd.date_range('1951-04-01', '1952-01-01', freq='QS'))
#mod_hamilton = sm.tsa.MarkovAutoregression(dta_hamilton, k_regimes=2, order=4, switching_ar=False)
#res_hamilton = mod_hamilton.fit()
#print("FILTERED_MARGINAL_PROBABILITY\n")
#print(res_hamilton.filtered_marginal_probabilities[0])
temp2.write(str(compid[i]) + '\t')
for k in range(0, comp_yr_match[i]):
temp2.write(str(compyr[l]) + '\t')
l = l + 1
temp2.write('\n')
continue
elif len(final) == 1:
#dta_hamilton = pd.Series(final, index=pd.date_range('1951-04-01', '1952-04-01', freq='QS'))
#mod_hamilton = sm.tsa.MarkovAutoregression(dta_hamilton, k_regimes=2, order=4, switching_ar=False)
#res_hamilton = mod_hamilton.fit()
#print("FILTERED_MARGINAL_PROBABILITY\n")
#print(res_hamilton.filtered_marginal_probabilities[0])
temp2.write(str(compid[i]) + '\t')
for k in range(0, comp_yr_match[i]):
temp2.write(str(compyr[l]) + '\t')
l = l + 1
temp2.write('\n')
continue
elif len(final) == 2:
#dta_hamilton = pd.Series(final, index=pd.date_range('1951-04-01', '1952-07-01', freq='QS'))
#mod_hamilton = sm.tsa.MarkovAutoregression(dta_hamilton, k_regimes=2, order=4, switching_ar=False)
#res_hamilton = mod_hamilton.fit()
#print("FILTERED_MARGINAL_PROBABILITY\n")
#print(res_hamilton.filtered_marginal_probabilities[0])
temp2.write(str(compid[i]) + '\t')
for k in range(0, comp_yr_match[i]):
temp2.write(str(compyr[l]) + '\t')
l = l + 1
temp2.write('\n')
continue
elif len(final) == 3:
#dta_hamilton = pd.Series(final, index=pd.date_range('1951-04-01', '1951-10-01', freq='QS'))
#mod_hamilton = sm.tsa.MarkovAutoregression(dta_hamilton, k_regimes=2, order=4, switching_ar=False)
#res_hamilton = mod_hamilton.fit()
#print("FILTERED_MARGINAL_PROBABILITY\n")
#print(res_hamilton.filtered_marginal_probabilities[0])
temp2.write(str(compid[i]) + '\t')
for k in range(0, comp_yr_match[i]):
temp2.write(str(compyr[l]) + '\t')
l = l + 1
temp2.write('\n')
continue
elif len(final) == 4:
#dta_hamilton = pd.Series(final, index=pd.date_range('1951-04-01', '1952-01-01', freq='QS'))
#mod_hamilton = sm.tsa.MarkovAutoregression(dta_hamilton, k_regimes=2, order=4, switching_ar=False)
#res_hamilton = mod_hamilton.fit()
#print("FILTERED_MARGINAL_PROBABILITY\n")
#print(res_hamilton.filtered_marginal_probabilities[0])
temp2.write(str(compid[i]) + '\t')
for k in range(0, comp_yr_match[i]):
temp2.write(str(compyr[l]) + '\t')
l = l + 1
temp2.write('\n')
continue
elif len(final) == 5:
#dta_hamilton = pd.Series(final, index=pd.date_range('1951-04-01', '1952-04-01', freq='QS'))
#mod_hamilton = sm.tsa.MarkovAutoregression(dta_hamilton, k_regimes=2, order=4, switching_ar=False)
#res_hamilton = mod_hamilton.fit()
#print("FILTERED_MARGINAL_PROBABILITY\n")
#print(res_hamilton.filtered_marginal_probabilities[0])
temp2.write(str(compid[i]) + '\t')
for k in range(0, comp_yr_match[i]):
temp2.write(str(compyr[l]) + '\t')
l = l + 1
temp2.write('\n')
continue
elif len(final) == 6:
#dta_hamilton = pd.Series(final, index=pd.date_range('1951-04-01', '1952-07-01', freq='QS'))
#mod_hamilton = sm.tsa.MarkovAutoregression(dta_hamilton, k_regimes=2, order=4, switching_ar=False)
#res_hamilton = mod_hamilton.fit()
#print("FILTERED_MARGINAL_PROBABILITY\n")
#print(res_hamilton.filtered_marginal_probabilities[0])
temp2.write(str(compid[i]) + '\t')
for k in range(0, comp_yr_match[i]):
temp2.write(str(compyr[l]) + '\t')
l = l + 1
temp2.write('\n')
continue
elif len(final) == 7:
#dta_hamilton = pd.Series(final, index=pd.date_range('1951-04-01', '1952-10-01', freq='QS'))
#mod_hamilton = sm.tsa.MarkovAutoregression(dta_hamilton, k_regimes=2, order=4, switching_ar=False)
#res_hamilton = mod_hamilton.fit()
#print("FILTERED_MARGINAL_PROBABILITY\n")
#print(res_hamilton.filtered_marginal_probabilities[0])
temp2.write(str(compid[i]) + '\t')
for k in range(0, comp_yr_match[i]):
temp2.write(str(compyr[l]) + '\t')
l = l + 1
temp2.write('\n')
continue
elif len(final) == 8:
#dta_hamilton = pd.Series(final, index=pd.date_range('1951-04-01', '1953-01-01', freq='QS'))
#mod_hamilton = sm.tsa.MarkovAutoregression(dta_hamilton, k_regimes=2, order=4, switching_ar=False)
#res_hamilton = mod_hamilton.fit()
#print("FILTERED_MARGINAL_PROBABILITY\n")
#print(res_hamilton.filtered_marginal_probabilities[0])
temp2.write(str(compid[i]) + '\t')
for k in range(0, comp_yr_match[i]):
temp2.write(str(compyr[l]) + '\t')
l = l + 1
temp2.write('\n')
continue
elif len(final) == 9:
#dta_hamilton = pd.Series(final, index=pd.date_range('1951-04-01', '1953-04-01', freq='QS'))
#mod_hamilton = sm.tsa.MarkovAutoregression(dta_hamilton, k_regimes=2, order=4, switching_ar=False)
#res_hamilton = mod_hamilton.fit()
#print("FILTERED_MARGINAL_PROBABILITY\n")
#print(res_hamilton.filtered_marginal_probabilities[0])
temp2.write(str(compid[i]) + '\t')
for k in range(0, comp_yr_match[i]):
temp2.write(str(compyr[l]) + '\t')
l = l + 1
temp2.write('\n')
continue
elif len(final) == 10:
#dta_hamilton = pd.Series(final, index=pd.date_range('1951-04-01', '1953-07-01', freq='QS'))
#mod_hamilton = sm.tsa.MarkovAutoregression(dta_hamilton, k_regimes=2, order=4, switching_ar=False)
#res_hamilton = mod_hamilton.fit()
#print("FILTERED_MARGINAL_PROBABILITY\n")
#print(res_hamilton.filtered_marginal_probabilities[0])
temp2.write(str(compid[i]) + '\t')
for k in range(0, comp_yr_match[i]):
temp2.write(str(compyr[l]) + '\t')
l = l + 1
print('\n')
continue
elif len(final) == 11:
dta_hamilton = pd.Series(final, index=pd.date_range('1951-04-01', '1953-10-01', freq='QS'))
mod_hamilton = sm.tsa.MarkovAutoregression(dta_hamilton, k_regimes=2, order=4, switching_ar=False)
res_hamilton = mod_hamilton.fit()
print("FILTERED_MARGINAL_PROBABILITY\n")
temp2.write(str(compid[i]) + '\t')
for k in range(0, comp_yr_match[i]):
temp2.write(str(compyr[l]) + '\t')
l = l + 1
temp2.write('\n')
temp2.write('\t')
for j in range(0, len(res_hamilton.filtered_marginal_probabilities[0])):
temp2.write(str(res_hamilton.filtered_marginal_probabilities[0][j]) + '\t')
temp2.write('\n')
elif len(final) == 12:
dta_hamilton = pd.Series(final, index=pd.date_range('1951-04-01', '1954-01-01', freq='QS'))
mod_hamilton = sm.tsa.MarkovAutoregression(dta_hamilton, k_regimes=2, order=4, switching_ar=False)
res_hamilton = mod_hamilton.fit()
print("FILTERED_MARGINAL_PROBABILITY\n")
temp2.write(str(compid[i]) + '\t')
for k in range(0, comp_yr_match[i]):
temp2.write(str(compyr[l]) + '\t')
l = l + 1
temp2.write('\n')
temp2.write('\t')
for j in range(0, len(res_hamilton.filtered_marginal_probabilities[0])):
temp2.write(str(res_hamilton.filtered_marginal_probabilities[0][j]) + '\t')
temp2.write('\n')
elif len(final) == 13:
dta_hamilton = pd.Series(final, index=pd.date_range('1951-04-01', '1954-04-01', freq='QS'))
mod_hamilton = sm.tsa.MarkovAutoregression(dta_hamilton, k_regimes=2, order=4, switching_ar=False)
res_hamilton = mod_hamilton.fit()
print("FILTERED_MARGINAL_PROBABILITY\n")
temp2.write(str(compid[i]) + '\t')
for k in range(0, comp_yr_match[i]):
temp2.write(str(compyr[l]) + '\t')
l = l + 1
temp2.write('\n')
temp2.write('\t')
for j in range(0, len(res_hamilton.filtered_marginal_probabilities[0])):
temp2.write(str(res_hamilton.filtered_marginal_probabilities[0][j]) + '\t')
temp2.write('\n')
elif len(final) == 14:
dta_hamilton = pd.Series(final, index=pd.date_range('1951-04-01', '1954-07-01', freq='QS'))
mod_hamilton = sm.tsa.MarkovAutoregression(dta_hamilton, k_regimes=2, order=4, switching_ar=False)
res_hamilton = mod_hamilton.fit()
print("FILTERED_MARGINAL_PROBABILITY\n")
temp2.write(str(compid[i]) + '\t')
for k in range(0, comp_yr_match[i]):
temp2.write(str(compyr[l]) + '\t')
l = l + 1
temp2.write('\n')
temp2.write('\t')
for j in range(0, len(res_hamilton.filtered_marginal_probabilities[0])):
temp2.write(str(res_hamilton.filtered_marginal_probabilities[0][j]) + '\t')
temp2.write('\n')
elif len(final) == 15:
dta_hamilton = pd.Series(final, index=pd.date_range('1951-04-01', '1954-10-01', freq='QS'))
mod_hamilton = sm.tsa.MarkovAutoregression(dta_hamilton, k_regimes=2, order=4, switching_ar=False)
res_hamilton = mod_hamilton.fit()
print("FILTERED_MARGINAL_PROBABILITY\n")
temp2.write(str(compid[i]) + '\t')
for k in range(0, comp_yr_match[i]):
temp2.write(str(compyr[l]) + '\t')
l = l + 1
temp2.write('\n')
temp2.write('\t')
for j in range(0, len(res_hamilton.filtered_marginal_probabilities[0])):
temp2.write(str(res_hamilton.filtered_marginal_probabilities[0][j]) + '\t')
temp2.write('\n')
elif len(final) == 16:
dta_hamilton = pd.Series(final, index=pd.date_range('1951-04-01', '1955-01-01', freq='QS'))
mod_hamilton = sm.tsa.MarkovAutoregression(dta_hamilton, k_regimes=2, order=4, switching_ar=False)
res_hamilton = mod_hamilton.fit()
print("FILTERED_MARGINAL_PROBABILITY\n")
temp2.write(str(compid[i]) + '\t')
for k in range(0, comp_yr_match[i]):
temp2.write(str(compyr[l]) + '\t')
l = l + 1
temp2.write('\n')
temp2.write('\t')
for j in range(0, len(res_hamilton.filtered_marginal_probabilities[0])):
temp2.write(str(res_hamilton.filtered_marginal_probabilities[0][j]) + '\t')
temp2.write('\n')
elif len(final) == 17:
dta_hamilton = pd.Series(final, index=pd.date_range('1951-04-01', '1955-04-01', freq='QS'))
mod_hamilton = sm.tsa.MarkovAutoregression(dta_hamilton, k_regimes=2, order=4, switching_ar=False)
res_hamilton = mod_hamilton.fit()
print("FILTERED_MARGINAL_PROBABILITY\n")
temp2.write(str(compid[i]) + '\t')
for k in range(0, comp_yr_match[i]):
temp2.write(str(compyr[l]) + '\t')
l = l + 1
temp2.write('\n')
temp2.write('\t')
for j in range(0, len(res_hamilton.filtered_marginal_probabilities[0])):
temp2.write(str(res_hamilton.filtered_marginal_probabilities[0][j]) + '\t')
temp2.write('\n')
elif len(final) == 18:
dta_hamilton = pd.Series(final, index=pd.date_range('1951-04-01', '1955-07-01', freq='QS'))
mod_hamilton = sm.tsa.MarkovAutoregression(dta_hamilton, k_regimes=2, order=4, switching_ar=False)
res_hamilton = mod_hamilton.fit()
print("FILTERED_MARGINAL_PROBABILITY\n")
temp2.write(str(compid[i]) + '\t')
for k in range(0, comp_yr_match[i]):
temp2.write(str(compyr[l]) + '\t')
l = l + 1
temp2.write('\n')
temp2.write('\t')
for j in range(0, len(res_hamilton.filtered_marginal_probabilities[0])):
temp2.write(str(res_hamilton.filtered_marginal_probabilities[0][j]) + '\t')
temp2.write('\n')
elif len(final) == 19:
dta_hamilton = pd.Series(final, index=pd.date_range('1951-04-01', '1955-10-01', freq='QS'))
mod_hamilton = sm.tsa.MarkovAutoregression(dta_hamilton, k_regimes=2, order=4, switching_ar=False)
res_hamilton = mod_hamilton.fit()
print("FILTERED_MARGINAL_PROBABILITY\n")
temp2.write(str(compid[i]) + '\t')
for k in range(0, comp_yr_match[i]):
temp2.write(str(compyr[l]) + '\t')
l = l + 1
temp2.write('\n')
temp2.write('\t')
for j in range(0, len(res_hamilton.filtered_marginal_probabilities[0])):
temp2.write(str(res_hamilton.filtered_marginal_probabilities[0][j]) + '\t')
temp2.write('\n')
elif len(final) == 20:
dta_hamilton = pd.Series(final, index=pd.date_range('1951-04-01', '1956-01-01', freq='QS'))
mod_hamilton = sm.tsa.MarkovAutoregression(dta_hamilton, k_regimes=2, order=4, switching_ar=False)
res_hamilton = mod_hamilton.fit()
print("FILTERED_MARGINAL_PROBABILITY\n")
temp2.write(str(compid[i]) + '\t')
for k in range(0, comp_yr_match[i]):
temp2.write(str(compyr[l]) + '\t')
l = l + 1
temp2.write('\n')
temp2.write('\t')
for j in range(0, len(res_hamilton.filtered_marginal_probabilities[0])):
temp2.write(str(res_hamilton.filtered_marginal_probabilities[0][j]) + '\t')
temp2.write('\n')
elif len(final) == 21:
dta_hamilton = pd.Series(final, index=pd.date_range('1951-04-01', '1956-04-01', freq='QS'))
mod_hamilton = sm.tsa.MarkovAutoregression(dta_hamilton, k_regimes=2, order=4, switching_ar=False)
res_hamilton = mod_hamilton.fit()
print("FILTERED_MARGINAL_PROBABILITY\n")
temp2.write(str(compid[i]) + '\t')
for k in range(0, comp_yr_match[i]):
temp2.write(str(compyr[l]) + '\t')
l = l + 1
temp2.write('\n')
temp2.write('\t')
for j in range(0, len(res_hamilton.filtered_marginal_probabilities[0])):
temp2.write(str(res_hamilton.filtered_marginal_probabilities[0][j]) + '\t')
temp2.write('\n')
else:
continue
#dta_hamilton = pd.Series(rgnp, index=pd.date_range('1951-04-01', '1984-10-01', freq='QS'))
# Plot the data
#dta_hamilton.plot(title='Growth rate of Real GNP', figsize=(12,3))
# Fit the model
#mod_hamilton = sm.tsa.MarkovAutoregression(dta_hamilton, k_regimes=2, order=4, switching_ar=False)
#res_hamilton = mod_hamilton.fit()
#res_hamilton.summary()
#fig, axes = plt.subplots(2, figsize=(7,7))
#ax = axes[0]
#ax.plot(res_hamilton.filtered_marginal_probabilities[0])
#ax.fill_between(usrec.index, 0, 1, where=usrec['USREC'].values, color='k', alpha=0.1)
#ax.set_xlim(dta_hamilton.index[4], dta_hamilton.index[-1])
#ax.set(title='Filtered probability of recession')
#ax = axes[1]
#ax.plot(res_hamilton.smoothed_marginal_probabilities[0])
#ax.fill_between(usrec.index, 0, 1, where=usrec['USREC'].values, color='k', alpha=0.1)
#ax.set_xlim(dta_hamilton.index[4], dta_hamilton.index[-1])
#ax.set(title='Smoothed probability of recession')
#fig.tight_layout()
#print(res_hamilton.expected_durations)
temp.close()
temp2.close()