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Table5.py
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Table5.py
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__author__ = 'runwei_zhang'
__author__ = 'runwei_zhang'
import csv
import pandas as pd
import numpy as np
from numpy.linalg import inv
import os
def partitionNACE():
df = pd.read_csv('data/data.csv',usecols=['Firmid','Realdate','Nace2','Event','Bigevent', 'Upevent', 'Bigeventcount','Dmarket', 'Dprice','Nace'],low_memory=False)
df = df.groupby('Firmid').filter(lambda x: len(x) ==456)
for naceid,nacegroup in df.groupby('Nace2'):
Nlen = 0
# for firmid,firmgroup in nacegroup.groupby('Firmid'):
# if not firmgroup['Dprice'].isnull().values.any():
# Nlen += 1
nacegroup.to_csv('data/Nace2/%s.csv'%naceid)
def Column1(df,Nlen,Tlen):
mA = np.zeros((Nlen*Tlen,Nlen*2+1),float)
vb = np.zeros(Nlen*Tlen)
i = 0
for firmid,firmgroup in df.groupby('Firmid'):
if not firmgroup['Dprice'].isnull().values.any():
mA[i*Tlen:(i+1)*Tlen,i] = np.ones(Tlen)
mA[i*Tlen:(i+1)*Tlen,i+Nlen] = firmgroup['Dmarket'].values
mA[i*Tlen:(i+1)*Tlen,2*Nlen] = firmgroup['Event'].values
vb[i*Tlen:(i+1)*Tlen] = [p2f(x) for x in firmgroup['Dprice'].values]
i += 1
tmpp = inv(mA.T.dot(mA)).dot(mA.T)
# tmpp = np.dot(tmpp2,)
Xhat = tmpp.dot(vb)
gamma = Xhat[-1]
print gamma
return gamma
def Column2(df,Nlen,Tlen):
mA = np.zeros((Nlen*Tlen,Nlen*2+2),float)
vb = np.zeros(Nlen*Tlen)
i = 0
for firmid,firmgroup in df.groupby('Firmid'):
if not firmgroup['Dprice'].isnull().values.any():
mA[i*Tlen:(i+1)*Tlen,i] = np.ones(Tlen)
mA[i*Tlen:(i+1)*Tlen,i+Nlen] = firmgroup['Dmarket'].values
mA[i*Tlen:(i+1)*Tlen,2*Nlen] = firmgroup['Event'].values
mA[i*Tlen:(i+1)*Tlen,1+2*Nlen] = np.multiply(firmgroup['Do'].values,firmgroup['Event'].values)
vb[i*Tlen:(i+1)*Tlen] = [p2f(x) for x in firmgroup['Dprice'].values]
i += 1
tmpp = inv(mA.T.dot(mA)).dot(mA.T)
Xhat = tmpp.dot(vb)
gamma = Xhat[-2:]
print gamma
return gamma
def Column3(df,Nlen,Tlen):
mA = np.zeros((Nlen*Tlen,Nlen*2+2),float)
vb = np.zeros(Nlen*Tlen)
i = 0
for firmid,firmgroup in df.groupby('Firmid'):
if not firmgroup['Dprice'].isnull().values.any():
mA[i*Tlen:(i+1)*Tlen,i] = np.ones(Tlen)
mA[i*Tlen:(i+1)*Tlen,i+Nlen] = firmgroup['Dmarket'].values
mA[i*Tlen:(i+1)*Tlen,2*Nlen] = firmgroup['Event'].values
mA[i*Tlen:(i+1)*Tlen,1+2*Nlen] = np.multiply(firmgroup['Di'].values,firmgroup['Event'].values)
vb[i*Tlen:(i+1)*Tlen] = [p2f(x) for x in firmgroup['Dprice'].values]
i += 1
tmpp = inv(mA.T.dot(mA)).dot(mA.T)
Xhat = tmpp.dot(vb)
gamma = Xhat[-2:]
print gamma
return gamma
def Column4(df,Nlen,Tlen,misdict):
mA = np.zeros((Nlen*Tlen,Nlen*2+3),float)
vb = np.zeros(Nlen*Tlen)
i = 0
for firmid,firmgroup in df.groupby('Firmid'):
if not firmgroup['Dprice'].isnull().values.any():
mA[i*Tlen:(i+1)*Tlen,i] = np.ones(Tlen)
mA[i*Tlen:(i+1)*Tlen,i+Nlen] = firmgroup['Dmarket'].values
mA[i*Tlen:(i+1)*Tlen,2*Nlen] = firmgroup['Event'].values
eu = firmgroup['Conc'].values
where_are_NaNs = np.isnan(eu)
eu[where_are_NaNs] = 0
mis = []
for x in firmgroup['Nace2'].values:
print x,misdict[x]
mis.append(misdict[x])
# mis = firmgroup['Dumconc'].values
# where_are_NaNs = np.isnan(mis)
# mis[where_are_NaNs] = 0
mA[i*Tlen:(i+1)*Tlen,1+2*Nlen] = np.multiply(eu,firmgroup['Event'].values)
mA[i*Tlen:(i+1)*Tlen,2+2*Nlen] = np.multiply(mis,firmgroup['Event'].values)
vb[i*Tlen:(i+1)*Tlen] = [p2f(x) for x in firmgroup['Dprice'].values]
i += 1
tmpp = inv(mA.T.dot(mA)).dot(mA.T)
Xhat = tmpp.dot(vb)
gamma = Xhat[-3:]
print gamma
return gamma
def Column5(df,Nlen,Tlen):
mA = np.zeros((Nlen*Tlen,Nlen*2+3),float)
vb = np.zeros(Nlen*Tlen)
i = 0
for firmid,firmgroup in df.groupby('Firmid'):
if not firmgroup['Dprice'].isnull().values.any():
mA[i*Tlen:(i+1)*Tlen,i] = np.ones(Tlen)
mA[i*Tlen:(i+1)*Tlen,i+Nlen] = firmgroup['Dmarket'].values
mA[i*Tlen:(i+1)*Tlen,2*Nlen] = firmgroup['Event'].values
mA[i*Tlen:(i+1)*Tlen,1+2*Nlen] = np.multiply(firmgroup['Do'].values,firmgroup['Event'].values)
mA[i*Tlen:(i+1)*Tlen,2+2*Nlen] = np.multiply(firmgroup['Di'].values,firmgroup['Event'].values)
vb[i*Tlen:(i+1)*Tlen] = [p2f(x) for x in firmgroup['Dprice'].values]
i += 1
tmpp = inv(mA.T.dot(mA)).dot(mA.T)
Xhat = tmpp.dot(vb)
gamma = Xhat[-3:]
print gamma
return gamma
def Column6(df,Nlen,Tlen):
mA = np.zeros((Nlen*Tlen,Nlen*2+6),float)
vb = np.zeros(Nlen*Tlen)
i = 0
for firmid,firmgroup in df.groupby('Firmid'):
if not firmgroup['Dprice'].isnull().values.any():
mA[i*Tlen:(i+1)*Tlen,i] = np.ones(Tlen)
mA[i*Tlen:(i+1)*Tlen,i+Nlen] = firmgroup['Dmarket'].values
mA[i*Tlen:(i+1)*Tlen,2*Nlen] = firmgroup['Event'].values
eu = firmgroup['Conc'].values
where_are_NaNs = np.isnan(eu)
eu[where_are_NaNs] = 0
mis = firmgroup['Dumconc'].values
where_are_NaNs = np.isnan(mis)
mis[where_are_NaNs] = 0
mA[i*Tlen:(i+1)*Tlen,1+2*Nlen] = np.multiply(firmgroup['Do'].values,firmgroup['Event'].values)
mA[i*Tlen:(i+1)*Tlen,2+2*Nlen] = np.multiply(eu,firmgroup['Event'].values)
mA[i*Tlen:(i+1)*Tlen,3+2*Nlen] = np.multiply(mis,firmgroup['Event'].values)
mA[i*Tlen:(i+1)*Tlen,4+2*Nlen] = np.multiply(np.multiply(eu,firmgroup['Event'].values),firmgroup['Do'].values)
mA[i*Tlen:(i+1)*Tlen,5+2*Nlen] = np.multiply(np.multiply(mis,firmgroup['Event'].values),firmgroup['Do'].values)
vb[i*Tlen:(i+1)*Tlen] = [p2f(x) for x in firmgroup['Dprice'].values]
i += 1
tmpp = inv(mA.T.dot(mA)).dot(mA.T)
Xhat = tmpp.dot(vb)
gamma = Xhat[-6:]
print gamma
return gamma
def Column7(df,Nlen,Tlen):
mA = np.zeros((Nlen*Tlen,Nlen*2+6),float)
vb = np.zeros(Nlen*Tlen)
i = 0
for firmid,firmgroup in df.groupby('Firmid'):
if not firmgroup['Dprice'].isnull().values.any():
mA[i*Tlen:(i+1)*Tlen,i] = np.ones(Tlen)
mA[i*Tlen:(i+1)*Tlen,i+Nlen] = firmgroup['Dmarket'].values
mA[i*Tlen:(i+1)*Tlen,2*Nlen] = firmgroup['Event'].values
eu = firmgroup['Conc'].values
where_are_NaNs = np.isnan(eu)
eu[where_are_NaNs] = 0
mis = firmgroup['Dumconc'].values
where_are_NaNs = np.isnan(mis)
mis[where_are_NaNs] = 0
mA[i*Tlen:(i+1)*Tlen,1+2*Nlen] = np.multiply(firmgroup['Di'].values,firmgroup['Event'].values)
mA[i*Tlen:(i+1)*Tlen,2+2*Nlen] = np.multiply(eu,firmgroup['Event'].values)
mA[i*Tlen:(i+1)*Tlen,3+2*Nlen] = np.multiply(mis,firmgroup['Event'].values)
mA[i*Tlen:(i+1)*Tlen,4+2*Nlen] = np.multiply(np.multiply(eu,firmgroup['Event'].values),firmgroup['Di'].values)
mA[i*Tlen:(i+1)*Tlen,5+2*Nlen] = np.multiply(np.multiply(mis,firmgroup['Event'].values),firmgroup['Di'].values)
vb[i*Tlen:(i+1)*Tlen] = [p2f(x) for x in firmgroup['Dprice'].values]
i += 1
tmpp = inv(mA.T.dot(mA)).dot(mA.T)
Xhat = tmpp.dot(vb)
gamma = Xhat[-6:]
print gamma
return gamma
def Column8(df,Nlen,Tlen):
mA = np.zeros((Nlen*Tlen,Nlen*2+9),float)
vb = np.zeros(Nlen*Tlen)
i = 0
for firmid,firmgroup in df.groupby('Firmid'):
if not firmgroup['Dprice'].isnull().values.any():
mA[i*Tlen:(i+1)*Tlen,i] = np.ones(Tlen)
mA[i*Tlen:(i+1)*Tlen,i+Nlen] = firmgroup['Dmarket'].values
mA[i*Tlen:(i+1)*Tlen,2*Nlen] = firmgroup['Event'].values
eu = firmgroup['Conc'].values
where_are_NaNs = np.isnan(eu)
eu[where_are_NaNs] = 0
mis = firmgroup['Dumconc'].values
where_are_NaNs = np.isnan(mis)
mis[where_are_NaNs] = 0
mA[i*Tlen:(i+1)*Tlen,1+2*Nlen] = np.multiply(firmgroup['Do'].values,firmgroup['Event'].values)
mA[i*Tlen:(i+1)*Tlen,2+2*Nlen] = np.multiply(firmgroup['Di'].values,firmgroup['Event'].values)
mA[i*Tlen:(i+1)*Tlen,3+2*Nlen] = np.multiply(eu,firmgroup['Event'].values)
mA[i*Tlen:(i+1)*Tlen,4+2*Nlen] = np.multiply(mis,firmgroup['Event'].values)
mA[i*Tlen:(i+1)*Tlen,5+2*Nlen] = np.multiply(np.multiply(eu,firmgroup['Event'].values),firmgroup['Do'].values)
mA[i*Tlen:(i+1)*Tlen,6+2*Nlen] = np.multiply(np.multiply(mis,firmgroup['Event'].values),firmgroup['Do'].values)
mA[i*Tlen:(i+1)*Tlen,7+2*Nlen] = np.multiply(np.multiply(eu,firmgroup['Event'].values),firmgroup['Di'].values)
mA[i*Tlen:(i+1)*Tlen,8+2*Nlen] = np.multiply(np.multiply(mis,firmgroup['Event'].values),firmgroup['Di'].values)
vb[i*Tlen:(i+1)*Tlen] = [p2f(x) for x in firmgroup['Dprice'].values]
i += 1
tmpp = inv(mA.T.dot(mA)).dot(mA.T)
Xhat = tmpp.dot(vb)
gamma = Xhat[-9:]
print gamma
return gamma
def p2f(x):
return float(x.strip('%'))/100
if __name__ == "__main__":
# with open('data/data2.csv') as csvfile:
# reader = csv.reader(csvfile,delimiter=',')
# for i in xrange(0,100):
# row = next(reader)
# print row
df = pd.read_csv('data/data.csv')
Nlen = 0
Tlen =456
for firmid,firmgroup in df.groupby('Firmid'):
if not firmgroup['Dprice'].isnull().values.any():
Nlen += 1
with open('data/NoEE.csv') as csvfile:
reader = csv.DictReader(csvfile,delimiter=',')
misdict =dict()
for row in reader:
misdict[int(row['NACE'])]=float(row['NoEE'])
Column4(df,Nlen,Tlen,misdict)