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MasterCodeofCashmereProject-main without imputation.Rmd
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---
title: "CoefPlots of ParallelTrendsTest"
author: "Peng Sun"
date: "10/03/2022"
output: html_document
---
#Load packages
```{r load packages, message=FALSE, warning=FALSE}
#install.packages("clubSandwich")
library(readr)
library(did)
library(fixest)
library(DRDID)
library(tidyverse)
library(plm)
library(stargazer)
library(sandwich)
library(lmtest)
library(jtools)
library(clubSandwich)
```
#read data
```{r}
setwd("~/R")
df2012<-read_csv("~/R/df2012_20210920.csv")
```
```{r}
# set levels of timedummy variables so that Y2012 will be used as
names(df2012)
unique(df2012$timedummy)
```
```{r}
df2012$timedummy<-fct_relevel(df2012$timedummy,levels="Y2012","Y2013","Y2014","Y2015","Y2016","Y2017","janapr18","t1","t2")
df2012$groups<-fct_relevel(df2012$groups, levels="C","A","B","D")
#set levels of groups dummies so that Group C will be used as base
unique(df2012$groups)
# set levels of period dummies so that t0 period will be used as base
df2012$periods<-fct_relevel(df2012$periods, levels="t0","t1","t2")
df2012<-df2012%>%select(-1)
names(df2012)
```
```{r}
df2012<-df2012 %>%
mutate(timedummy2=case_when(year==2012~"Y2012",
year==2013~"Y2013",
year==2014~"Y2014",
year==2015~"Y2015",
year==2016~"Y2016",
year==2017~"Y2017",
janapr18==1~"Y2018janapr",
t1==1~"Yt1",
t2==1~"Yt2"))
df2012<- df2012%>%
mutate(timedummy3=as.factor(yearqrt))
class(df2012$timedummy3)
unique(df2012$timedummy3)
unique(df2012$timedummy2)
df2012<-df2012%>%
mutate(timedummy6= case_when(timedummy3=="2012 Q1"~ "Y2012Q1",
timedummy3=="2012 Q2"~ "Y2012Q2",
timedummy3=="2012 Q3"~ "Y2012Q3",
timedummy3=="2012 Q4"~ "Y2012Q4",
timedummy3=="2013 Q1"~ "Y2013Q1",
timedummy3=="2013 Q2"~ "Y2013Q2",
timedummy3=="2013 Q3"~ "Y2013Q3",
timedummy3=="2013 Q4"~ "Y2013Q4",
timedummy3=="2014 Q1"~ "Y2014Q1",
timedummy3=="2014 Q2"~ "Y2014Q2",
timedummy3=="2014 Q3"~ "Y2014Q3",
timedummy3=="2014 Q4"~ "Y2014Q4",
timedummy3=="2015 Q1"~ "Y2015Q1",
timedummy3=="2015 Q2"~ "Y2015Q2",
timedummy3=="2015 Q3"~ "Y2015Q3",
timedummy3=="2015 Q4"~ "Y2015Q4",
timedummy3=="2016 Q1"~ "Y2016Q1",
timedummy3=="2016 Q2"~ "Y2016Q2",
timedummy3=="2016 Q3"~ "Y2016Q3",
timedummy3=="2016 Q4"~ "Y2016Q4",
timedummy3=="2017 Q1"~ "Y2017Q1",
timedummy3=="2017 Q2"~ "Y2017Q2",
timedummy3=="2017 Q3"~ "Y2017Q3",
timedummy3=="2017 Q4"~ "Y2017Q4",
janapr18==1~"Y2018janapr",
t1==1~"Yt2",
t2==1~"Yt3"
))
unique(df2012$timedummy6)
df2012$timedummy6<-fct_relevel(df2012$timedummy6,levels="Y2012Q1","Y2012Q2","Y2012Q3","Y2012Q4","Y2013Q1","Y2013Q2","Y2013Q3","Y2013Q4","Y2014Q1","Y2014Q2","Y2014Q3","Y2014Q4","Y2015Q1","Y2015Q2","Y2015Q3","Y2015Q4","Y2016Q1","Y2016Q2","Y2016Q3","Y2016Q4","Y2017Q1","Y2017Q2","Y2017Q3","Y2017Q4","Y2018janapr","Yt2","Yt3")
```
```{r}
df2012$timedummy2<-fct_relevel(df2012$timedummy2, levels="Y2018janapr","Y2012","Y2013","Y2014","Y2015","Y2016","Y2017","Yt1","Yt2")
```
```{r}
levels(df2012$timedummy2)
```
# With 2012 dataset
#column 1
```{r}
plm_1<-plm(lp~t1+t2+A+B+D+t1*A+t2*A+t1*B+t2*B+t1*D+t2*D,data=df2012, model="pooling", index=c("houseid","year") )
summary(plm_1)
```
```{r}
coefplot(plm_1,keep=c("t1:A","t2:A","t1:B","t2:B","t1:D","t2:D"), ylim=c(-0.4,0.4), axis.text.x = element_text(size=14))
```
#Column 2
```{r}
lm_housefeature2<-lm(lp~t1+t2+t1*A+t2*A+t1*B+t2*B+t1*D+t2*D+floor_area+land_area+bedrooms_min+as.factor(building_age)+ as.factor(deck)+ as.factor(wall_material)
+as.factor(roof_material)+as.factor(garages_mainroof)+ as.factor(contour)+as.factor(timedummy2) +as.factor(suburb),data=df2012)
summary(lm_housefeature2)
```
```{r}
coefplot(lm_housefeature2,keep=c("t1:A","t2:A","t1:B","t2:B","t1:D","t2:D"), ylim=c(-0.4,0.4), axis.text.x = element_text(size=14))
```
#Column 3, these two are the same
```{r}
plm_year<-plm(lp~yr2012+yr2013+yr2014+yr2015+yr2016+yr2017+t1+t2+A+B+D+t1*A+t2*A+t1*B+t2*B+t1*D+t2*D,data=df2012, model="within",effect="individual", index=c("houseid","year"))
summary(plm_year)
```
```{r}
coefplot(plm_year,keep=c("t1:A","t2:A","t1:B","t2:B","t1:D","t2:D"), ylim=c(-0.4,0.4), axis.text.x = element_text(size=14))
```
```{r}
#plm_year_coef<-coef_test(plm_year, vcov="CR2", cluster=df2012$houseid, test="Satterthwaite")
```
# Column 4
```{r}
housefeature_pretrends<-plm(lp~yr2012+yr2013+yr2014+yr2015+yr2016+yr2017+yr2012*A+yr2013*A+yr2014*A+yr2015*A+yr2016*A+yr2017*A+yr2012*B+yr2013*B+yr2014*B+yr2015*B+yr2016*B+yr2017*B+yr2012*D+yr2013*D+yr2014*D+yr2015*D+yr2016*D+yr2017*D+t1+t2+A+B+D+t1*A+t2*A+t1*B+t2*B+t1*D+t2*D+floor_area+land_area+bedrooms_min+as.factor(building_age)+ as.factor(deck)+ as.factor(wall_material)
+as.factor(roof_material)+as.factor(garages_mainroof)+ as.factor(contour)+as.factor(suburb),data=df2012,model="pooling", index=c("houseid","year"))
summary(housefeature_pretrends)
```
#calculate counterfactual values of lp for C as if groupC is also treated (losing access to CHS)
```{r}
#testC<-subset(df2012, df2012$C==1)
#install.pacakges()
```
#coef graph of Column 4
```{r}
coefplot(housefeature_pretrends,ylim=c(-0.4,0.4), keep=c("yr2012:A","yr2013:A","yr2014:A","yr2015:A","yr2016:A","yr2017:A","A:t1", "A:t2"), axis.text.x = element_text(size=14))
```
```{r}
coefplot(housefeature_pretrends,keep=c("yr2012:B","yr2013:B","yr2014:B","yr2015:B","yr2016:B","yr2017:B", "B:t1","B:t2"), axis.text.x = element_text(size=14))
```
```{r}
coefplot(housefeature_pretrends, keep=c("yr2012:D","yr2013:D","yr2014:D","yr2015:D","yr2016:D","yr2017:D", "D:t1", "D:t2"), axis.text.x = element_text(size=14))
```
# Column 5, pretrends controlled, with 2018JanApr and C as baseline
```{r}
plm_year_house_parallel2018<-plm(lp~yr2012+yr2013+yr2014+yr2015+yr2016+yr2017+yr2012*A+yr2013*A+yr2014*A+yr2015*A+yr2016*A+yr2017*A+yr2012*B+yr2013*B+yr2014*B+yr2015*B+yr2016*B+yr2017*B+yr2012*D+yr2013*D+yr2014*D+yr2015*D+yr2016*D+yr2017*D+t1+t2+A+B+D+t1*A+t2*A+t1*B+t2*B+t1*D+t2*D,data=df2012, model="within",effect="individual", index=c("houseid","year"))
summary(plm_year_house_parallel2018)
```
```{r}
#plm_year_parallel_coef<-coef_test(plm_year_house_parallel2018, vcov="CR2", cluster=df2012$houseid, test="Satterthwaite")
```
```{r}
pooltest(housefeature_pretrends,plm_year_house_parallel2018)
```
```{r}
pooltest(plm_year_house_parallel2018, housefeature_pretrends)
```
#Coef graph of column 5
```{r}
coefplot(plm_year_house_parallel2018, keep=c("yr2012:A","yr2013:A","yr2014:A","yr2015:A","yr2016:A","yr2017:A","janapr18:A"), axis.text.x = element_text(size=14))
```
```{r}
coefplot(plm_year_house_parallel2018,keep=c("yr2012:B","yr2013:B","yr2014:B","yr2015:B","yr2016:B","yr2017:B"), axis.text.x = element_text(size=14))
```
```{r}
coefplot(plm_year_house_parallel2018, keep=c("yr2012:D","yr2013:D","yr2014:D","yr2015:D","yr2016:D","yr2017:D"), axis.text.x = element_text(size=14))
```
```{r}
# transfer coefficients into percentage for the parameters of interest
exp(-0.031601)-1#[1] -0.03110691
exp(-0.14608)-1 # -0.1359114
```
# A Vs C, T1 Vs T2+T3
```{r}
# Column 1
AC_did1<-plm(lp ~ I(t1 + t2) + A * I(t1 + t2), data = df2012_AC,
model = "pooling",index=c("houseid","year") )
summary(AC_did1)
```
```{r}
#Column 2
lm_housefeatureAC<-lm(lp~I(t1 + t2) + A * I(t1 + t2)+floor_area+land_area+bedrooms_min+as.factor(building_age)+ as.factor(deck)+ as.factor(wall_material)
+as.factor(roof_material)+as.factor(garages_mainroof)+ as.factor(contour)+as.factor(timedummy2) +as.factor(suburb),data=df2012_AC_mutated)
summary(lm_housefeatureAC)
```
```{r}
dim(df2012_AC_mutated)
8090-18
```
```{r}
#column 3
plm_AC3<-plm(lp~I(t1 + t2) + A * I(t1 + t2)+yr2013+yr2014+yr2015+yr2016+yr2017+janapr18,data=df2012_AC, model="within",effect="individual", index=c("houseid","year"))
summary(plm_AC3)
```
```{r}
# Column 4
housefeature_pretrendsAC<-plm(lp~I(t1 + t2) +A+ I(t1 + t2)*A+yr2012+yr2013+yr2014+yr2015+yr2016+yr2017+yr2012*A+yr2013*A+yr2014*A+yr2015*A+yr2016*A+yr2017*A+floor_area+land_area+bedrooms_min+as.factor(building_age)+ as.factor(deck)+ as.factor(wall_material)
+as.factor(roof_material)+as.factor(garages_mainroof)+ as.factor(contour)+as.factor(suburb),data=df2012_AC_mutated,model="pooling", index=c("houseid","year"))
summary(housefeature_pretrendsAC)
```
```{r}
# Column5
plm_year_house_parallel2018AC<-plm(lp~yr2012+yr2013+yr2014+yr2015+yr2016+yr2017+yr2012*A+yr2013*A+yr2014*A+yr2015*A+yr2016*A+yr2017*A+I(t1 + t2) +A+ A * I(t1 + t2),data=df2012_AC, model="within",effect="individual", index=c("houseid","year"))
summary(plm_year_house_parallel2018AC)
```
# generate graph for Column 4
```{r}
coefplot(housefeature_pretrendsAC,keep=c("A:yr2012","A:yr2013","A:yr2014","A:yr2015","A:yr2016","A:yr2017"), axis.text.x = element_text(size=14))
```
```{r}
coefplot(plm_year_house_parallel2018AC,
keep=c("yr2012:A","yr2013:A","yr2014:A","yr2015:A","yr2016:A","yr2017:A"), axis.text.x = element_text(size=14))
```
# BC T1+2 Vs T3
```{r}
#column 1
BCdid<-lm(lp~B*t2,data=df2012_BC_3p)
summary(BCdid)
```
```{r}
#Column 2
lm_housefeatureBC<-lm(lp~B*t2+as.factor(timedummy2)+floor_area+land_area+bedrooms_min+as.factor(building_age)+ as.factor(deck)+ as.factor(wall_material)
+as.factor(roof_material)+as.factor(garages_mainroof)+ as.factor(contour) +as.factor(suburb),data=df2012_BC_3p_mutated)
summary(lm_housefeatureBC)
```
```{r}
plm_BC4<-plm(lp~ B *t2+as.factor(timedummy2),data=df2012_BC_3p, model="within",effect="individual", index=c("houseid","year"))
summary(plm_BC4)
```
```{r}
#Column 3
plm_BC3<-plm(lp~ B *t2+yr2013+yr2014+yr2015+yr2016+yr2017+janapr18+t1+t2+B,data=df2012_BC_3p, model="within",effect="individual", index=c("houseid","year"))
summary(plm_BC3)
```
```{r}
#Column 4
housefeature_pretrendsBC<-plm(lp~yr2012+yr2013+yr2014+yr2015+yr2016+yr2017+janapr18+yr2012*B+yr2013*B+yr2014*B+yr2015*B+yr2016*B+yr2017*B+janapr18*B+ B * t2+floor_area+land_area+bedrooms_min+as.factor(building_age)+ as.factor(deck)+ as.factor(wall_material)
+as.factor(roof_material)+as.factor(garages_mainroof)+ as.factor(contour)+as.factor(suburb),data=df2012_BC_3p_mutated,model="pooling", index=c("houseid","year"))
summary(housefeature_pretrendsBC)
```
```{r}
coefplot(housefeature_pretrendsBC,keep=c("yr2012:B","yr2013:B","yr2014:B","yr2015:B","yr2016:B","yr2017:B","janapr18:B"), axis.text.x = element_text(size=14))
```
```{r}
# Column5
unique(df2012$timedummy)
plm_year_house_parallelBC<-plm(lp~yr2012+yr2013+yr2014+yr2015+yr2016+yr2017+janapr18+yr2012*B+yr2013*B+yr2014*B+yr2015*B+yr2016*B+yr2017*B+janapr18*B+ B * t2,data=df2012_BC_3p, model="within",effect="individual", index=c("houseid","year"))
summary(plm_year_house_parallelBC)
```
```{r}
coefplot(plm_year_house_parallelBC,keep=c("yr2012:B","yr2013:B","yr2014:B","yr2015:B","yr2016:B","yr2017:B","janapr18:B"), axis.text.x = element_text(size=14))
```
# Check how many houses are sold before and after each downsizing
```{r}
#generate two subsets of houses from A sold pre and post
houseAt0<-df2012%>% filter(A==1) %>% filter (t0==1) #1091 sales from 912 houses
dim(houseAt0)
length(unique(houseAt0$houseid))
houseAt1<-df2012%>% filter(A==1) %>% filter (t1==1) #257 sales from 254 houses
dim(houseAt1)
length(unique(houseAt1$houseid))
houseAt1t2<-df2012%>% filter(A==1) %>% filter (t1==1|t2==1) #507 sales from 487 houses
dim(houseAt1t2)
length(unique(houseAt1t2$houseid))
houseAt2<-df2012%>% filter(A==1) %>% filter (t2==1) #250 sales from 248 houses
dim(houseAt2)
length(unique(houseAt2$houseid))
t2A<-unique(houseAt2$houseid)
y<-unique(houseAt0$houseid)
resultAt0t2<-t2A %in% y
sort(resultAt0t2,decreasing=TRUE) #117 houses found sold in both t0 and t2
# generate two vectors for houseid from both subsets
y<-unique(houseAt0$houseid)#1091
x<-unique(houseAt1$houseid) #257
x2<-unique(houseAt1t2$houseid) #487
# check if houseid from post also appeared in
? "%in%"
resultAt1<-x %in% y # return True& False, 92 TRUEs returned
resultAt1t2<-x2 %in% y # 203 TRUES Returned
sort(resultAt1t2,decreasing=TRUE)
sort(resultAt1, decreasing=TRUE)
# example of %in% usage
#m<-c(1,12,23,45)
#n<-c(11,12,122,123,145)
#m %in% n
```
```{r}
#generate two subsets of houses from A sold pre and post
houseBt0<-df2012%>% filter(B==1) %>% filter (t0==1) #857 sales from 735 houses
dim(houseBt0)
length(unique(houseBt0$houseid))
houseBt1<-df2012%>% filter(B==1) %>% filter (t1==1) #194 sales from 187 houses
dim(houseBt1)
length(unique(houseBt1$houseid))
houseBt2<-df2012%>% filter(B==1) %>% filter (t2==1) #185 sales from 183 houses
dim(houseBt2)
length(unique(houseBt2$houseid))
houseBt1t2<-df2012%>% filter(B==1) %>% filter (t1==1|t2==1) #379 sales from 358 houses
dim(houseBt1t2)
length(unique(houseBt1t2$houseid))
# generate two vectors for houseid from both subsets
yB<-unique(houseBt0$houseid)
xB<-unique(houseBt1$houseid)
t2B<-unique(houseBt2$houseid)
resultBt0t2<-t2B %in% yB
sort(resultBt0t2,decreasing=TRUE) #82 houses found with sales in both t0 and t2
xB %in% yB #78 trues returned
xB2<-unique(houseBt1t2$houseid)
# check if houseid from post also appeared in
? "%in%"
resultBt1t2<-xB2 %in% yB # 155 TRUES Returned
sort(resultBt1t2,decreasing=TRUE)
```
```{r eval=FALSE, include=FALSE}
#generate two subsets of houses from A sold pre and post
houseCt0<-df2012%>% filter(C==1) %>% filter (t0==1) #4399 sales from 3746 houses
dim(houseCt0)
length(unique(houseCt0$houseid))
houseCt1<-df2012%>% filter(C==1) %>% filter (t1==1) #1110 sales from 1089 houses
dim(houseCt1)
length(unique(houseCt1$houseid))
houseCt1t2<-df2012%>% filter(C==1) %>% filter (t1==1|t2==1) # 2093 sales from 1976 houses
dim(houseCt1t2)
length(unique(houseCt1t2$houseid))
houseCt2<-df2012%>% filter(C==1) %>% filter (t2==1) #983 sales from 960 houses
dim(houseCt2)
length(unique(houseCt2$houseid))
t2C<-unique(houseCt2$houseid)
yC<-unique(houseCt0$houseid)
xC<-unique(houseCt1$houseid)
resultCt0t2<-t2C %in% yC
sort(resultCt0t2,decreasing=TRUE) # 388 houses with sales in both t0 and t2
# generate two vectors for houseid from both subsets
resultCt1<-xC %in% yC # trues returned
View(resultCt1)
sort(resultCt1, decreasing=TRUE) #418TRUES
xC2<-unique(houseCt1t2$houseid)
# check if houseid from post also appeared in
? "%in%"
resultCt1t2<-xC2 %in% yC # 782 TRUES Returned
sort(resultCt1t2,decreasing=TRUE)
```
```{r eval=FALSE, include=FALSE}
#generate two subsets of houses from A sold pre and post
houseDt0<-df2012%>% filter(D==1) %>% filter (t0==1) #3710 sales from 3123 houses
dim(houseDt0)
length(unique(houseDt0$houseid))
houseDt1<-df2012%>% filter(D==1) %>% filter (t1==1) #923 sales from 897 houses
dim(houseDt1)
length(unique(houseDt1$houseid))
houseDt1t2<-df2012%>% filter(D==1) %>% filter (t1==1|t2==1) # 1702 sales from 1602 houses
dim(houseDt1t2)
length(unique(houseDt1t2$houseid))
houseDt2<-df2012%>% filter(D==1) %>% filter (t2==1) #799 sales from 763 houses
dim(houseDt2)
length(unique(houseDt2$houseid))
t2D<-unique(houseDt2$houseid)
yD<-unique(houseDt0$houseid)
xD<-unique(houseDt1$houseid)
resultDt0t2<-t2D %in% yD
sort(resultDt0t2,decreasing=TRUE) # 361 houses with sales in both t0 and t2
# generate two vectors for houseid from both subsets
resultDt1<-xD %in% yD # trues returned
View(resultDt1)
sort(resultDt1, decreasing=TRUE) #367TRUES
xD2<-unique(houseDt1t2$houseid)
# check if houseid from post also appeared in
? "%in%"
resultDt1t2<-xD2 %in% yD # 706 TRUES Returned
sort(resultDt1t2,decreasing=TRUE)
```