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Chap1_DGP.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$timedummy,levels="Y2012","Y2013","Y2014","Y2015","Y2016","Y2017","janapr18","t1","t2")
```
```{r}
df2012<-df2012 %>%
mutate(timedummy2=case_when(year==2012~"Y2012",
year==2013~"Y2013",
year==2014~"Y2014",
year==2015~"Y2015",
year==2016~"Y2016",
year==2017~"Y2017",
janapr18==1~"Y2018janapr",
t1==1~"Yt1",
t2==1~"Yt2"))
df2012<- df2012%>%
mutate(timedummy3=as.factor(yearqrt))
class(df2012$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$timedummy2,levels="Y2018janapr", "Y2012", "Y2013","Y2014","Y2015","Y2016","Y2017","Yt1", "Yt2")
```
```{r}
df2012_mutated <- df2012
df2012_mutated$contour=replace_na(df2012_mutated$contour,"Unreported")
df2012_mutated$deck=replace_na(df2012_mutated$deck,"Unreported")
# df2012_mutated$garages_mainroof=replace_na(df2012_mutated$garages_mainroof,"Unreported")
df2012_mutated$roof_material=replace_na(df2012_mutated$roof_material,"Unreported")
df2012_mutated$wall_material=replace_na(df2012_mutated$wall_material,"Unreported")
df2012_mutated$building_age=replace_na(df2012_mutated$building_age,"Unreported")
df2012_mutated$land_area=replace_na(df2012_mutated$land_area,0.069)
df2012_mutated$floor_area=replace_na(df2012_mutated$floor_area,136)
```
```{r}
garage=df2012_mutated$garages_mainroof
# replace msising values with Unreported
garage[is.na(garage)]<-"Unreported"
df2012_mutated$garages_mainroof<-garage
```
```{r}
bedrooms_min=df2012_mutated$bedrooms_min
# replace msising values with Unreported
bedrooms_min[is.na(bedrooms_min)]<-"Unreported"
#head(bedrooms_min)
df2012_mutated$bedrooms_min<- bedrooms_min
```
# create new period dummies:
```{r}
library(lubridate)
# Transform string into date format
# 28 Oct, 2016 Engagement 1 date
# April 2017, report 1 date,
# April 24 2017, OIA1 date
# Jul 6, 2017 Media 1 date
# Oct 2017, report 2 date
# Dec 21, 2017, OIA 2 date
# Feb 1, 2018 Media Statements from Canterbury West Coast Secondary Principals Association
# Feb 26, 2018 report 2 date, OIA 2 reply
# Mar 21, 2018. Media 2 date
date_report1 <- "2017-04-01" # Assumpe both reports released on the first date of that month
date_report2 <- "2017-10-01"
date_engagement1<-"2016-10-28"
date_engagement2<- "2017-09-22"
date_schools_statement<-"2018-02-01"
date_media_1<-"2017-07-06"
date_media_2<-"2018-03-21"
```
# create new dummy variables of periods for further robustness checks
```{r}
df2012 <-df2012 %>%
mutate(period_engagement1 = case_when(df2012$sale_date_new < ymd(date_engagement1) ~ "t1_engagement1",
df2012$sale_date_new < ymd("2019-11-19") ~ "t2_engagement1",
TRUE ~ "t3_engagement1"
)) %>%
mutate(period_engagement2 = case_when(df2012$sale_date_new < ymd(date_engagement2) ~ "t1_engagement2",
df2012$sale_date_new < ymd("2019-11-19") ~ "t2_engagement2",
TRUE ~ "t3_engagement2"
)) %>%
mutate(period_report1 = case_when(df2012$sale_date_new < ymd(date_report1) ~ "t1_report1",
df2012$sale_date_new < ymd("2019-11-19") ~ "t2_report1",
TRUE ~ "t3_report1")) %>%
mutate(period_report2 = case_when(df2012$sale_date_new < ymd(date_report2) ~ "t1_report2",
df2012$sale_date_new < ymd("2019-11-19") ~ "t2_report2",
TRUE ~ "t3_report2")) %>%
mutate(period_schoolstatement = case_when(df2012$sale_date_new < ymd(date_schools_statement) ~ "t1_schoolstatement",
df2012$sale_date_new < ymd("2019-11-19") ~ "t2_schoolstatement",
TRUE ~ "t3_schoolstatement"))%>%
mutate(period_media2 = case_when(df2012$sale_date_new < ymd(date_media_2) ~ "t1_media2",
df2012$sale_date_new < ymd("2019-11-19") ~ "t2_media2",
TRUE ~ "t3_media2"
))%>%
mutate(period_media1 = case_when(df2012$sale_date_new < ymd(date_media_1) ~ "t1_media1",
df2012$sale_date_new < ymd("2019-11-19") ~ "t2_media1",
TRUE ~ "t3_media1"
))
```
#create dummy variables for all three periods in different scenarioes
```{r}
df2012 <- df2012 %>%
mutate(t1_engagement1 = case_when(period_engagement1 == "t1_engagement1" ~ 1,
TRUE ~ 0))%>%
mutate(t2_engagement1 = case_when(period_engagement1 == "t2_engagement1" ~ 1,
TRUE ~ 0))%>%
mutate(t3_engagement1 = case_when(period_engagement1 == "t3_engagement1" ~ 1,
TRUE ~ 0))%>%
mutate(t1_engagement2 = case_when(period_engagement2 == "t1_engagement2" ~ 1,
TRUE ~ 0))%>%
mutate(t2_engagement2 = case_when(period_engagement2 == "t2_engagement2" ~ 1,
TRUE ~ 0))%>%
mutate(t3_engagement2 = case_when(period_engagement2 == "t3_engagement2" ~ 1,
TRUE ~ 0))%>%
mutate(t1_report1 = case_when(period_report1 == "t1_report2" ~ 1,
TRUE ~ 0))%>%
mutate(t2_report1 = case_when(period_report1 == "t2_report2" ~ 1,
TRUE ~ 0))%>%
mutate(t3_report1 = case_when(period_report1 == "t3_report2" ~ 1,
TRUE ~ 0))%>%
mutate(t1_report2 = case_when(period_report2 == "t1_report2" ~ 1,
TRUE ~ 0))%>%
mutate(t2_report2 = case_when(period_report2 == "t2_report2" ~ 1,
TRUE ~ 0))%>%
mutate(t3_report2 = case_when(period_report2 == "t3_report2" ~ 1,
TRUE ~ 0))%>%
mutate(t1_media1 = case_when(period_media1 == "t1_media2" ~ 1,
TRUE ~ 0))%>%
mutate(t2_media1 = case_when(period_media1 == "t2_media2" ~ 1,
TRUE ~ 0))%>%
mutate(t3_media1 = case_when(period_media1 == "t3_media2" ~ 1,
TRUE ~ 0))%>%
mutate(t1_media2 = case_when(period_media2 == "t1_media2" ~ 1,
TRUE ~ 0))%>%
mutate(t2_media2 = case_when(period_media2 == "t2_media2" ~ 1,
TRUE ~ 0))%>%
mutate(t3_media2 = case_when(period_media2 == "t3_media2" ~ 1,
TRUE ~ 0))
```
```{r}
names(df2012)
```
```{r}
mean(df2012$lp)
```
```{r}
lp2<-log(df2012$salesprice)
mean(lp2)
```
```{r}
mean(df2012$salesprice)
```
```{r}
df2012_mutated <- df2012
df2012_mutated$contour=replace_na(df2012_mutated$contour,"Unreported")
df2012_mutated$deck=replace_na(df2012_mutated$deck,"Unreported")
# df2012_mutated$garages_mainroof=replace_na(df2012_mutated$garages_mainroof,"Unreported")
df2012_mutated$roof_material=replace_na(df2012_mutated$roof_material,"Unreported")
df2012_mutated$wall_material=replace_na(df2012_mutated$wall_material,"Unreported")
df2012_mutated$building_age=replace_na(df2012_mutated$building_age,"Unreported")
df2012_mutated$land_area=replace_na(df2012_mutated$land_area,0.069)
df2012_mutated$floor_area=replace_na(df2012_mutated$floor_area,136)
```
```{r}
garage=df2012_mutated$garages_mainroof
# replace msising values with Unreported
garage[is.na(garage)]<-"Unreported"
df2012_mutated$garages_mainroof<-garage
```
#Get the same obs for the pooled cross section models(fix the 30 obs difference)
```{r}
bedrooms_min=df2012_mutated$bedrooms_min
# replace msising values with Unreported
bedrooms_min[is.na(bedrooms_min)]<-"Unreported"
#head(bedrooms_min)
df2012_mutated$bedrooms_min<- bedrooms_min
```
```{r}
ggplot(data = df2012) +
geom_bar(mapping = aes(x = periods, fill = groups), position = "dodge") +
scale_fill_grey() +
theme(axis.text.x = element_text(angle = 0, size = 14),
plot.title = element_text(size = 16),
axis.title = element_text(size = 16))
```
```{r}
ggplot(data = df2012) +
geom_bar(mapping = aes(x = groups, fill = periods), position = "dodge") +
scale_fill_manual(values = c("#1b9e77", "#d95f02", "#7570b3"),
breaks = c("before", "during", "after"),
labels = c("T1", "T2", "T3")) +
theme(axis.text.x = element_text(angle = 0, size = 14),
plot.title = element_text(size = 16),
axis.title = element_text(size = 16)) +
labs(x = "Groups", y = "Count", fill = "Periods")
```
```{r}
ggplot(data = df2012) +
geom_bar(mapping = aes(x = periods, fill = groups), position = "dodge") +
scale_fill_manual(values = c("#1b9e77", "#d95f02", "#7570b3"),
breaks = c("group1", "group2", "group3"),
labels = c("Group 1", "Group 2", "Group 3")) +
theme(axis.text.x = element_text(angle = 0, size = 14),
plot.title = element_text(size = 16),
axis.title = element_text(size = 16)) +
labs(x = "Periods", y = "Count", fill = "Groups")
```