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Chap1_DGP+drop missing values.Rmd
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---
title: "DGP_Chapter1"
author: "Peng"
date: "21/03/2023"
output: html_document
---
---
title: "More robustness test"
author: "Peng"
date: "2023/3/10"
output:
pdf_document: default
html_document: default
---
```{r}
library(readr)
library(did)
library(fixest)
library(DRDID)
library(tidyverse)
library(plm)
library(stargazer)
library(sandwich)
library(lmtest)
library(jtools)
library(clubSandwich)
library(lubridate)
```
```{r}
setwd("~/R")
df2012<-read_csv("~/R/df2012_20210920_datefixed.csv")
```
```{r}
df2012$sale_date_splited<- strsplit(df2012$sale_date, "/")
df2012$sale_date_y<- sapply(df2012$sale_date_splited, function(x) x[[3]])
df2012$sale_date_m<- sapply(df2012$sale_date_splited, function(x) x[[2]])
df2012$sale_date_d<- sapply(df2012$sale_date_splited, function(x) x[[1]])
df2012 <- df2012 %>%
mutate(sale_date_q = case_when(
month %in% 1:3 ~ "Q1",
month %in% 4:6 ~ "Q2",
month %in% 7:9 ~ "Q3",
month %in% 10:12 ~ "Q4"
))
date_new<-df2012$sale_date_splited
date_new<-lapply(date_new, function(x) as.Date(paste(x[3], x[2], x[1], sep = "-"), format = "%Y-%m-%d"))
head(date_new)
df2012$sale_date_new<-date_new
```
```{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
df2012$groups_D<-fct_relevel(df2012$groups, levels="D","A","B","C")
#set levels of groups_D 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$periods, levels="t0","t1","t2")
```
```{r}
df2012$timedummy<-fct_relevel(df2012$timedummy,levels="Y2012","Y2013","Y2014","Y2015","Y2016","Y2017","janapr18","t1","t2")
```
```{r}
subset<-df2012 %>% subset(janapr18==1 & C ==1)
length(subset)
mean(subset$lp)
```
```{r}
subset2<-df2012 %>% subset(t0==1 & C ==1)
length(subset2)
mean(subset2$lp)
```
```{r}
df2012<-df2012 %>%
mutate(timedummy2=case_when(year==2012~"Y2012",
year==2013~"Y2013",
year==2014~"Y2014",
year==2015~"Y2015",
year==2016~"Y2016",
year==2017~"Y2017",
janapr18==1~"Y2018janapr",
t1==1~"Yt1",
t2==1~"Yt2"))
df2012<- df2012%>%
mutate(timedummy3=as.factor(yearqrt))
class(df2012$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$timedummy2,levels="Y2018janapr", "Y2012", "Y2013","Y2014","Y2015","Y2016","Y2017","Yt1", "Yt2")
```
```{r}
unique(df2012$timedummy2)
```
```{r}
subset1<-df2012[df2012$C == 1 & df2012$janapr18 == 1,]
mean(subset1$lp) # 13.09592
mean(subset1$salesprice) # 530026
```
```{r}
subset2<-df2012[df2012$C == 1 & df2012$t0 == 1,]
mean(subset2$lp) # 12.98693
mean(subset2$salesprice) #476369.7
```
df2012_AC
```{r}
subset3<-df2012_AC[df2012_AC$C == 1 & df2012_AC$t0 == 1,]
mean(subset3$lp) # 12.98693
mean(subset3$salesprice) # 476369.7
```
```{r}
subset5<-df2012_AC[df2012_AC$C == 1 & df2012_AC$janapr18 == 1,]
mean(subset5$lp) # 13.09592
mean(subset5$salesprice) # 530026
```
```{r}
subset4<-df2012_BC_3p[df2012_BC_3p$C == 1 & df2012_BC_3p$t2 != 1,]
mean(subset4$lp) # 13.0158
mean(subset4$salesprice) #489621
```
```{r}
subset6<-df2012_BC_3p[df2012_BC_3p$C == 1 & df2012_BC_3p$janapr18 == 1,]
mean(subset6$lp) # 13.09592
mean(subset6$salesprice) # 530026
```
```{r}
summary(df2012)
```
```{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_mutated)
summary(lm_housefeature2)
```
#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(contour)+as.factor(timedummy2) +as.factor(suburb),data=df2012)
summary(lm_housefeature2)
```
```{r}
housefeature_pretrends<-lm(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(contour)+as.factor(suburb),data=df2012)
summary(housefeature_pretrends)
```
```{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_mutated,model="pooling", index=c("houseid","year"))
summary(housefeature_pretrends)
```
```{r}
housefeature_pretrends<-lm(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_mutated)
summary(housefeature_pretrends)
```
```{r}
```
#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"), ylim=c(-0.4,0.4), 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"), ylim=c(-0.4,0.4), axis.text.x = element_text(size=14))
```
```{r}
#Table7 Panel B Col 2
#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)
```
```