-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathmain_3analysis.R
367 lines (291 loc) · 15.2 KB
/
main_3analysis.R
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
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
# ----
TEJ0 <- readDB(DB_fil = "TEJ1996.xlsx", attr_fil = "DB2.xlsx", attr_sht = "TEJ_attr", xls_sht = "TEJ")
TEJ8 <- TEJ0 %>% DBfilter() %>%
replaceNAby0(col=c('OERD','OEPRO','Land','LandR','CTP_IFRS_CFI','CTP_IFRS_CFO','CTP_IFRS_CFF','CTP_GAAP','INTAN','RELATIN')) %>%
dep_var() %>% STR() %>%
select(-c(market,TEJ_name2,TEJ_code2,TEJ_name1,TEJ_code1,CTP_GAAP,CTP_IFRS_CFI,CTP_IFRS_CFO,CTP_IFRS_CFF,STR_RD.lag,STR_EMP.lag,STR_MB.lag,STR_MARKET.lag,STR_PPE.lag)) %>%
fnHHI() %>% fnGDP(file="DB2.xlsx",col_sht="GDP_colnames",DB_sht="GDP")
# ----
write.csv(na_count(TEJ0),file="原始資料集缺漏值.csv",row.names=TRUE)
yr <- seq(2006,2015);yr0 <- seq(1996,2005)
write.csv(na_count(TEJ0 %>% filter(year(date) %in% yr)),file="原始資料2006.2015闕漏值.csv",row.names = TRUE)
write.csv(na_count(TEJ0 %>% filter(year(date) %in% yr0)),file="原始資料1996.2005闕漏值.csv",row.names = TRUE)
# ----
write.csv(TEJ8,"TEJ8.csv",row.names = T)
#write.csv(TEJ8.1,"TEJ8.1.csv",row.names = T)
writeLines("Wrote 'TEJ8.csv' in folder, and data is set to analyze,please confirm!\n100 % is completed...")
# ----
# TEJ8 = from 2001-2015
TEJ8 <- read.csv("TEJ8.csv",row.names = 1)
DB.orig <- dplyr::select(TEJ8, year,TSE_code,ETR,CETR,STR,HHI,HHI_Dum,ROA,SIZE,LEV,
INTANG,QUICK,EQINC,OUTINSTI,RELATIN,RELATOUT,RELAT,FAM_Dum,GDP,
RD,EMP,MB,MARKET,PPE)
DB.orig$RELAT2 <- DB.orig$RELATIN + DB.orig$RELATOUT
DB.orig$FAM_Dum <- as.factor(DB.orig$FAM_Dum)
DB$HHI_Dum <- DB$HHI_Dum + 1
#DB.orig$HHI_Dum <- as.factor(DB.orig$HHI_Dum)
str(DB.orig)
#scatterplotMatrix(DB.orig,diagonal='histogram',smoother=F)
# DB.wind = TEJ8.1
DB.orig <- replace(x = DB.orig,is.na(DB.orig),values = 0)
DB.orig.2006 <- DB.orig %>% filter(year %in% seq(2006,2015))
DB.wind <- winsamp(x="DB.orig",col=c('ROA','SIZE','LEV','INTANG','QUICK','EQINC',
'RELATIN','RELATOUT'),prob = 0.01)
DB.wind$ETR[DB.wind$ETR <= 0] <- 0; DB.wind$CETR[DB.wind$CETR <=0] <- 0
DB.wind$ETR[DB.wind$ETR >= 1] <- 1; DB.wind$CETR[DB.wind$CETR >=1] <- 1
DB.wind.2006 <- DB.wind %>% filter(year %in% seq(2006,2015))
DB.wind.2006 <- winsamp(x="DB.wind.2006",col=c('ROA','SIZE','LEV','INTANG','QUICK','EQINC','RELATIN','RELATOUT'),prob = 0.01)
# 分析STR變數優良性
DB <- DB.wind.2006[,c(3,4,5,7,8,9,10,11,13,14,19,25)]
DB$HHI_Dum <- as.factor(DB$HHI_Dum)
library(MASS)
lm <- lm(ETR ~ STR+HHI_Dum+ROA+SIZE+LEV+INTANG+QUICK+EQINC+OUTINSTI+RELAT2+FAM_Dum+GDP,DB.wind.2006)
summary(lm)
lm2 <- stepAIC(lm(ETR ~ STR+HHI_Dum+ROA+SIZE+LEV+INTANG+QUICK+EQINC+OUTINSTI+RELAT2+FAM_Dum+GDP,DB.wind.2006),direction = "both",k=log(nrow(DB)),steps = 1000)
summary(lm2)
DB$ETR = DB$ETR+1
lm3 <- lm(ETR ~ STR+HHI_Dum+ROA+SIZE+LEV+EQINC+OUTINSTI+RELAT2+GDP, DB)
summary(lm3)
plot(lm3,which=1)
bc <- boxcox(lm3)
bc$x[which.max(bc$y)]
# -2 ... so make ETR^-2
lm3.1 <- lm((ETR^-2) ~ STR+HHI_Dum+ROA+SIZE+LEV+EQINC+OUTINSTI+RELAT2+GDP, DB)
summary(lm3.1)
plot(lm3.1,which=1)
library(lmtest)
dwtest(lm3.1)
durbinWatsonTest(lm3.1)
#acf(lm3.1)
vif(lm3.1)
qqPlot(lm3.1,labels="lm3.1",id.method="identify"
,simulate=TRUE,main="Q-Q Plot")
lm3.2 <- lm((ETR^-2) ~ log(STR)+HHI_Dum+log(ROA+1)+log(SIZE)+log(LEV)+log(EQINC+1)+log(OUTINSTI+1)+log(RELAT2+1)+GDP, DB)
summary(lm3.2)
plot(lm3.2,which=1)
#lm3.2.0 <- stepAIC(lm((ETR^-2) ~ poly(log(STR),3)+HHI_Dum+poly(log(ROA+1),3)+poly(log(SIZE),3)+poly(log(LEV),3)+poly(log(EQINC+1),3)+poly(log(OUTINSTI+1),3)+poly(log(RELAT2+1),3)+poly(GDP,3), DB),direction = "backward",steps = 10000,k = log(nrow(DB)))
lm3.2.0 <- lm((ETR^-2) ~ log(STR)+HHI_Dum+log(SIZE)+log(LEV)+log(ROA+1)+log(EQINC+1)+log(OUTINSTI+1)+log(RELAT2+1)+GDP, DB)
lm3.2.1 <- lm((ETR^-2) ~ log(STR)^3+HHI_Dum+log(STR)^3:HHI_Dum+log(SIZE)^2+log(LEV)^2+log(ROA+1)^3*log(EQINC+1)*log(OUTINSTI+1)+log(RELAT2+1)+GDP, DB)
lm3.2.1.AIC <- stepAIC(lm3.2.1,steps = 1000,k=log(nrow(DB)),direction = "both")
lm3.2.2 <- lm((ETR^-2) ~ HHI_Dum+log(STR)^2:HHI_Dum+log(SIZE)^2+log(LEV)^2+log(ROA+1)^2+log(EQINC+1)+log(OUTINSTI+1)+log(RELAT2+1)+GDP, DB)
summary(lm3.2.1.AIC)
summary(lm3.2.2)
#lm3.2.3 <- lm((ETR^-2) ~ log(STR)^2:HHI_Dum+log(ROA+1)^2*log(EQINC+1)+log(OUTINSTI+1)+log(RELAT2+1)+GDP, DB)
#summary(lm3.2.3)
lmA <- lm((ETR^-2) ~ HHI_Dum+log(STR)^2:HHI_Dum+log(SIZE)^2+log(LEV)^2+
log(ROA+1)^2+log(EQINC+1)+log(OUTINSTI+1)+log(RELAT2+1)+GDP, DB)
lmB <- lm((ETR^-2) ~ HHI_Dum+log(STR)^2:HHI_Dum+log(SIZE)^2+log(LEV)^2+
log(ROA+1)^2+log(EQINC+1)+log(OUTINSTI+1)+log(RELAT2+1), DB)
anova(lmA,lmB)
xvar <- as.matrix(DB[,c(3,4,5,6,7,9,10,11,12)])
yvar <- DB$ETR
regsubsets(xvar,yvar,method="forward",nbest=1)
plot(lm3.2.2,which=1)
plot(lmB,which=1)
summary(lm(ETR^-2 ~ STR^2*HHI_Dum*SIZE^2 ,DB))
bartlett.test(lm3.2.2,DB)
#gdp-1
#relat-1
#outi-1
#eqinc-12
#lev-123
#size-123
#roa-123
newDB.wind.2006 <- as.data.frame(apply(dplyr::select(DB.wind.2006, ETR,CETR,STR,HHI,HHI_Dum,ROA,SIZE,LEV,
INTANG,QUICK,EQINC,OUTINSTI,RELATIN,RELATOUT,RELAT,FAM_Dum,GDP)
,MARGIN = 2, FUN = doNormalization))
newDB.wind.lmE <- lm(ETR ~ STR+HHI+ROA+SIZE+LEV+INTANG+QUICK+EQINC+OUTINSTI+RELAT+FAM_Dum+GDP
,newDB.wind.2006)
# scatter plot: ETR to each dependent variables
# ----
png("各變數與ETR之散佈圖.png",width = 1200,height=1000)
par(mfrow=c(3,4))
plot(DB$ETR,DB$STR)
plot(DB$ETR,DB$HHI_Dum)
plot(DB$ETR,DB$ROA)
plot(DB$ETR,DB$SIZE)
plot(DB$ETR,DB$LEV)
plot(DB$ETR,DB$INTANG)
plot(DB$ETR,DB$QUICK)
plot(DB$ETR,DB$EQINC)
plot(DB$ETR,DB$OUTINSTI)
plot(DB$ETR,DB$RELAT)
plot(DB$ETR,DB$FAM_Dum)
plot(DB$ETR,DB$GDP)
dev.off()
# ----
DB.wind.lmC <- lm(CETR ~ STR+HHI_Dum+ROA+SIZE+LEV+INTANG+QUICK+EQINC+OUTINSTI+RELAT+FAM_Dum+GDP
,DB.wind)
DB.wind.lmE.SH <- lm(ETR ~ STR+HHI_Dum+STR_HHI+ROA+SIZE+LEV+INTANG+QUICK+EQINC+OUTINSTI+RELAT+FAM_Dum+GDP,DB.wind)
DB.wind.lmC.SH <- lm(CETR ~ STR+HHI_Dum+STR_HHI+ROA+SIZE+LEV+INTANG+QUICK+EQINC+OUTINSTI+RELAT+FAM_Dum+GDP,DB.wind)
#TEJ8.1$STR_HHIsum <- TEJ8.1$STR * TEJ8.1$HHIsum # this is not a good idea!
DB.wind.2006.lmE <- lm(ETR ~ STR+HHI_Dum+ROA+SIZE+LEV+INTANG+QUICK+EQINC+OUTINSTI+RELAT+FAM_Dum+GDP
,DB.wind.2006)
DB.wind.2006.lmC <- lm(CETR ~ STR+HHI_Dum+ROA+SIZE+LEV+INTANG+QUICK+EQINC+OUTINSTI+RELAT+FAM_Dum+GDP
,DB.wind.2006)
DB.wind.2006.lmE.SH <- lm(ETR ~ STR+HHI_Dum+STR_HHI+ROA+SIZE+LEV+INTANG+QUICK+EQINC+OUTINSTI+RELAT+FAM_Dum+GDP
,DB.wind.2006)
DB.wind.2006.lmC.SH <- lm(CETR ~ STR+HHI_Dum+STR_HHI+ROA+SIZE+LEV+INTANG+QUICK+EQINC+OUTINSTI+RELAT+FAM_Dum+GDP
,DB.wind.2006)
TEJ8.2 <- TEJ8.1 %>% mutate(STR.PR = STR_RD.perank+STR_MB.perank+STR_EMP.perank+STR_PPE.perank+STR_MARKET.perank) %>% mutate(STR.PR_HHI = STR.PR*HHI_Dum)
lm.ETR2 <- lm(ETR ~ STR.PR+HHI_Dum+ROA+SIZE+LEV+INTANG+QUICK+EQINC+OUTINSTI+RELAT+FAM_Dum+GDP,TEJ8.2)
lm.CETR2 <- lm(CETR ~ STR.PR+HHI_Dum+ROA+SIZE+LEV+INTANG+QUICK+EQINC+OUTINSTI+RELAT+FAM_Dum+GDP,TEJ8.2)
lm.ETR.SH2 <- lm(ETR ~ STR.PR+HHI_Dum+STR.PR_HHI+ROA+SIZE+LEV+INTANG+QUICK+EQINC+OUTINSTI+RELAT+FAM_Dum+GDP,TEJ8.2)
lm.CETR.SH2 <- lm(CETR ~ STR.PR+HHI_Dum+STR.PR_HHI+ROA+SIZE+LEV+INTANG+QUICK+EQINC+OUTINSTI+RELAT+FAM_Dum+GDP,TEJ8.2)
# ----
DB.orig.Ecor <- corstars(DB.orig %>% select(ETR,STR,HHI_Dum,ROA,SIZE,LEV,
INTANG,QUICK,EQINC,OUTINSTI,RELAT,FAM_Dum,GDP)
,method = "pearson",removeTriangle = "lower",star = 3,result = "none")
DB.orig.Ccor <- corstars(DB.orig %>% select(CETR,STR,HHI_Dum,ROA,SIZE,LEV,
INTANG,QUICK,EQINC,OUTINSTI,RELAT,FAM_Dum,GDP)
,method = "pearson",removeTriangle = "lower",star = 3,result = "none")
DB.wind.Ecor <- corstars(DB.wind %>% select(ETR,STR,HHIsum,ROA,SIZE,LEV,
INTANG,QUICK,EQINC,OUTINSTI,RELAT,FAM_Dum,GDP)
,method = "pearson",removeTriangle = "lower",star = 3,result = "none")
DB.wind.Ccor <- corstars(DB.wind %>% select(CETR,STR,HHIsum,ROA,SIZE,LEV,
INTANG,QUICK,EQINC,OUTINSTI,RELAT,FAM_Dum,GDP)
,method = "pearson",removeTriangle = "lower",star = 3,result = "none")
# ----
# 檢驗LM等分散性:
require(car)
ncvTest(newDB.wind.lmE)
#Non-constant Variance Score Test
#Variance formula: ~ fitted.values
#Chisquare = 104.776 Df = 1 p = 1.36754e-24
# 卡方統計量、自由度、顯著性...
# p < 0.000, 拒絕虛無假設(B0=B1=B2...=0)
# 是否符合變異數等分散性?
# 檢驗殘差獨立性
durbinWatsonTest(newDB.wind.lmE)
#lag Autocorrelation D-W Statistic p-value
#1 0.001851732 1.996287 0.808
#Alternative hypothesis: rho != 0
# 殘差自我相關值、D-W統計量、顯著性
# 不顯著、殘差自我相關係數趨近0
# 常態性
# shapiro.test(residuals(DB.wind.lmE)) # 超過5000筆，不能用...用下面的圖
# residualPlot(DB.wind.lmE) = following line
plot(DB.wind.lmE,which=1)
# 校正模型
DB.wind.lmE.2 <- lm(ETR ~ STR+HHI+ROA+SIZE+LEV+INTANG+QUICK+EQINC+OUTINSTI+RELAT+FAM_Dum+GDP,newDB.orig)
plot(DB.wind.lmE.2,which=1)
DB.wind.lmE.SH.2 <- lm(ETR ~ STR+HHI_Dum+STR:HHI_Dum+ROA+SIZE+LEV+INTANG+QUICK+EQINC+OUTINSTI+RELAT+FAM_Dum+GDP
,DB.wind)
# check vif
#install.packages("car")
require(car)
vif(lm.ETR,digits=3)
vif(lm.CETR,digits=3)
vif(lm.ETR.SH,digits=3)
vif(lm.CETR.SH,digits=3)
# ----
TEJ8.3 <- merge(
merge(
x=TEJ8.1 %>% group_by(TSE_code,year) %>% summarise(NetSales.Deno = sum(as.numeric(NetSales),na.rm=TRUE)),
y=TEJ8.1 %>% group_by(TSE_code,year) %>% top_n(n=4,wt = NetSales) %>% summarise(NetSales.nume = sum(as.numeric(NetSales),na.rm=TRUE)),
by=c("TSE_code","year")) %>%
mutate(CR4 = as.numeric(NetSales.nume / NetSales.Deno))
,y=TEJ8.1
,by=c("TSE_code","year")
) %>% mutate(STR_CR4 = STR * CR4)
lm.ETR3 <- lm(ETR ~ STR+CR4+ROA+SIZE+LEV+INTANG+QUICK+EQINC+OUTINSTI+RELAT+FAM_Dum+GDP,TEJ8.3)
lm.CETR3 <- lm(CETR ~ STR+CR4+ROA+SIZE+LEV+INTANG+QUICK+EQINC+OUTINSTI+RELAT+FAM_Dum+GDP,TEJ8.3)
lm.ETR.SH3 <- lm(ETR ~ STR+CR4+STR_CR4+ROA+SIZE+LEV+INTANG+QUICK+EQINC+OUTINSTI+RELAT+FAM_Dum+GDP,TEJ8.3)
lm.CETR.SH3 <- lm(CETR ~ STR+CR4+STR_CR4+ROA+SIZE+LEV+INTANG+QUICK+EQINC+OUTINSTI+RELAT+FAM_Dum+GDP,TEJ8.3)
# ----
write(stargazer(
TEJ8.1 %>% select(ETR,CETR,STR,HHI_Dum,STR_HHI,ROA,SIZE,LEV,
INTANG,QUICK,EQINC,OUTINSTI,RELAT,FAM_Dum,GDP,
#,RD,EMP,MB,MARKET,PPE
STR_RD,STR_EMP,STR_MB,STR_MARKET,STR_PPE
)
,type = "html"
,summary.stat = c("n","min","median","max","sd","mean")
,align = TRUE
,notes.append = TRUE,label = "D1"
),"敘述統計表.html")
writeLines("Finish '敘述統計表.html' and print in folder.")
write.csv(ETR.cortab,"ETR.Pearson相關分析.csv")
write.csv(CETR.cortab,"CETR.Pearson相關分析.csv")
writeLines("Finish 'Pearson相關分析.csv' and print in folder.")
plottbA5 <- function(x=TEJ8.1){
x$TSE <- paste(x$TSE_code,x$TSE_name,sep="")
HHI_DB <- base::subset(x, select=c(TSE,year,HHI)) %>% distinct
HHI_DB$HHI <- replace(HHI_DB$HHI,HHI_DB$HHI >= 0.3,'高寡佔I 型')
HHI_DB$HHI <- replace(HHI_DB$HHI,HHI_DB$HHI < 0.3 & HHI_DB$HHI >= 0.18,'高寡佔II 型')
HHI_DB$HHI <- replace(HHI_DB$HHI,HHI_DB$HHI < 0.18 & HHI_DB$HHI >= 0.14,'低寡占I 型')
HHI_DB$HHI <- replace(HHI_DB$HHI,HHI_DB$HHI < 0.14 & HHI_DB$HHI >= 0.1,'低寡占II 型')
HHI_DB$HHI <- replace(HHI_DB$HHI,HHI_DB$HHI < 0.1 & HHI_DB$HHI >= 0.05,'競爭I 型')
HHI_DB$HHI <- replace(HHI_DB$HHI,HHI_DB$HHI < 0.05,'競爭II 型')
HHI_DB$HHI <- replace(HHI_DB$HHI,HHI_DB$HHI == "NaN","")
HHI_tbl <- dcast(HHI_DB,TSE ~ year) %>% as.data.frame
HHI_tbl <- as.data.frame(HHI_tbl[,-1],row.names=HHI_tbl[,1])
return(HHI_tbl)
}
HHI_tbl <- plottbA5()
write.csv(HHI_tbl,"產業年度架構表.csv")
writeLines("Finish '產業年度架構表.csv' and print in folder.")
writeLines("Finish Constructing Linear Model, part1.")
# Emp 1
write(stargazer(lm.ETR,lm.CETR,type="html",
dep.var.labels = c("$TAXAVO_{it}=ETR_{it}$","$TAXAVO_{it}=CashETR_{it}$"),
digits=3,label = "E1",align = TRUE),"實證一不含STR_HHI.html")
write(stargazer(lm.ETR.SH,lm.CETR.SH,type="html",
dep.var.labels = c("$TAXAVO_{it}=ETR_{it}$","$TAXAVO_{it}=CashETR_{it}$"),
digits=3,label = "E2",align = TRUE),"實證一含STR_HHI.html")
writeLines("Output 'Empirical 1(.html)' result in folder, please confirm.")
# ----
write(stargazer(lm.ETR.n1,lm.CETR.n1,type="html",
dep.var.labels = c("$TAXAVO_{it}=ETR_{it}$","$TAXAVO_{it}=CashETR_{it}$"),
digits=3,label = "E1",align = TRUE),"實證二(原始HHI)不含STR_HHIsum.html")
write(stargazer(lm.ETR.SH.n1,lm.CETR.SH.n1,type="html",
dep.var.labels = c("$TAXAVO_{it}=ETR_{it}$","$TAXAVO_{it}=CashETR_{it}$"),
digits=3,label = "E2",align = TRUE),"實證二(原始HHI)含STR_HHIsum.html")
write.csv(ETR.origHHIsum,"ETR.Pearson.原始HHI.csv")
write.csv(CETR.origHHIsum,"CETR.Pearson.原始HHI.csv")
# ----
writeLines("Finish Constructing Linear Model, part2.")
write(stargazer(lm.ETR2,lm.CETR2,
dep.var.labels = c("$TAXAVO_{it}=ETR_{it}$","$TAXAVO_{it}=CashETR_{it}$"),
digits=3,label = "F1",align = TRUE),"敏感分析一不含STR_HHI.html")
write(stargazer(lm.ETR.SH2,lm.CETR.SH2,
dep.var.labels = c("$TAXAVO_{it}=ETR_{it}$","$TAXAVO_{it}=CashETR_{it}$"),
digits=3,label = "F2",align = TRUE),"敏感分析一含STR_HHI.html")
writeLines("Output 'Empirical 2(.html)' result in folder, please confirm.")
# ----
writeLines("Finish Constructing Linear Model, part3.")
write(stargazer(lm.ETR3,lm.CETR3,
dep.var.labels = c("$TAXAVO_{it}=ETR_{it}$","$TAXAVO_{it}=CashETR_{it}$"),
digits=3,label = "F4",align = TRUE),"敏感分析二不含STR_HHI.html")
write(stargazer(lm.ETR.SH3,lm.CETR.SH3,
dep.var.labels = c("$TAXAVO_{it}=ETR_{it}$","$TAXAVO_{it}=CashETR_{it}$"),
digits=3,label = "F5",align = TRUE),"敏感分析二含STR_HHI.html")
writeLines("Output 'Empirical 3(.html)' result in folder, please confirm.")
# ----
tab.ETR <- summary(lm.ETR)$coefficients
tab.CETR <- summary(lm.CETR)$coefficients
tab.ETR2 <- summary(lm.ETR2)$coefficients
tab.CETR2 <- summary(lm.CETR2)$coefficients
tab.ETR3 <- summary(lm.ETR3)$coefficients
tab.CETR3 <- summary(lm.CETR3)$coefficients
tab.ETR.SH <- summary(lm.ETR.SH)$coefficients
tab.CETR.SH <- summary(lm.CETR.SH)$coefficients
tab.ETR.SH2 <- summary(lm.ETR.SH2)$coefficients
tab.CETR.SH2 <- summary(lm.CETR.SH2)$coefficients
tab.ETR.SH3 <- summary(lm.ETR.SH3)$coefficients
tab.CETR.SH3 <- summary(lm.CETR.SH3)$coefficients
colnames(tab.ETR) <- paste("ETR",colnames(tab.ETR),sep="_")
colnames(tab.CETR) <- paste("CETR",colnames(tab.CETR),sep="_")
colnames(tab.ETR.SH) <- paste("ETR.SH",colnames(tab.ETR.SH),sep="_")
colnames(tab.CETR.SH) <- paste("CETR.SH",colnames(tab.CETR.SH),sep="_")
colnames(tab.ETR2) <- paste("ETR.PR",colnames(tab.ETR2),sep="_")
colnames(tab.CETR2) <- paste("CTR.PR",colnames(tab.CETR2),sep="_")
colnames(tab.ETR.SH2) <- paste("ETR.PR.SH",colnames(tab.ETR.SH2),sep="_")
colnames(tab.CETR.SH2) <- paste("CETR.PR.SH",colnames(tab.CETR.SH2),sep="_")
colnames(tab.ETR3) <- paste("ETR.CR4",colnames(tab.ETR3),sep="_")
colnames(tab.CETR3) <- paste("CETR.CR4",colnames(tab.CETR3),sep="_")
colnames(tab.ETR.SH3) <- paste("ETR.CR4.SH",colnames(tab.ETR.SH3),sep="_")
colnames(tab.CETR.SH3) <- paste("CETR.CR4.SH",colnames(tab.CETR.SH3),sep="_")
#write(stargazer(cbind(tab.ETR,tab.CETR),type="html"),"tab.lm.html")
write.csv(cbind(tab.ETR,tab.CETR),"lm1.csv")
setwd("..")