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Self_Insurance_Regressions.Rmd
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Self_Insurance_Regressions.Rmd
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---
output: html_document
editor_options:
chunk_output_type: console
---
```{r setup, include=FALSE, echo=FALSE}
knitr::opts_chunk$set(echo = FALSE)
rm(list = ls()) # limpiar memoria
Sys.setlocale("LC_ALL","English")
source("libraries/load_packages.R")
fecha.salida <- format( as.Date(Sys.Date()) , "%Y%m%d" )
```
```{r Load the Data, echo=FALSE, message=FALSE, warning=FALSE}
DBSection3 <- read_csv2("outputs/self_insurance_db.csv")
# str(DBSection3)
DBSection3 <- DBSection3 %>% select(-ifscode, -wbcode2, -country_name_ifs)
```
```{r General Stats, include=FALSE}
# names(DBSection3)
stats_general <- stargazer(as.data.frame(DBSection3[,c(5:9,15:17)]),
type="html",
out="outputs/Self_insurance_Stats_Full_Sample.html",
title="Summary Statistics: Full Sample")
```
```{r filters, message=FALSE, warning=FALSE, include=FALSE}
clean_base <- subset (na.omit(DBSection3), year!=2008 & year!=2009
& rating>3
& spread<= 1000
& wbcode3 != "CHN"
& wbcode3 != "LBN")
# We add the row names for the outliers of the AvPlots show clear information
clean_base <- clean_base %>%
dplyr::mutate(
id = sprintf("%s:%s" , wbcode3 , format.Date(date, "%b-%y")))
row.names(clean_base) <- clean_base$id
sum_stats <- stargazer(as.data.frame(clean_base[,c(5:9,15:17)]),
type="html",
out="outputs/Self_Insurance_Stats_With_Filters.html",
title="Summary Statistics: With Filters")
```
```{r Countries and Period Covered, echo=FALSE, warning=FALSE}
country.list <- as.list(unique(clean_base$country_name_wb))
resume2 <- data.frame()
for (j in country.list) {
# j <- "ARG"
df <- subset(clean_base, country_name_wb == j)
obs <- nrow(df)
begins <- min(df$date)
ends <- max(df$date)
resume2.j <- data.frame(j, obs, begins, ends)
resume2 <- rbind(resume2, resume2.j)
}
colnames(resume2)[1] <- "Country"
countries_dates <- kable(resume2, "html",
col.names = c("Country", "obs", "Begins", "Ends"),
caption="Countries and Period Covered") %>%
kable_styling(bootstrap_options = c("striped", "hover", "responsive"),
full_width = F,
position = "left")
readr::write_file(countries_dates, "outputs/Self_Insurance_Countries_Dates.html")
```
```{r Regressions - full sample, echo=FALSE, message=FALSE, warning=FALSE, , echo=FALSE}
## Direct Effect, controlled by rating
R1 <- felm(lspread ~ lriskaversion + lrating + lus10y + R_Y + PuD_Y + PrD_Y
| country_name_wb| 0 | date,
data = clean_base)
## Same as R1, without private debt
R2 <- felm(lspread ~ lriskaversion + lrating + lus10y + R_Y + PuD_Y
| country_name_wb| 0 | date,
data = clean_base)
## Effect of reserves on sovereign ratings
R3 <- felm(lrating ~ lriskaversion + lus10y + R_Y + PuD_Y + PrD_Y
| country_name_wb| 0 | date,
data = clean_base)
## Direct Effect, not controlled by rating
R4 <- felm(lspread ~ lriskaversion + lus10y + R_Y + PuD_Y + PrD_Y
| country_name_wb| 0 | date,
data = clean_base)
```
```{r Regressions - Before the GFC, echo=FALSE, message=FALSE, warning=FALSE, , echo=FALSE}
# Before the GFC:
R5 <- felm(lspread ~ lriskaversion + lrating + lus10y + R_Y + PuD_Y + PrD_Y
| country_name_wb| 0 | date,
data = clean_base, subset = year<=2007)
R6 <- felm(lrating ~ lriskaversion + lus10y + R_Y + PuD_Y + PrD_Y
| country_name_wb| 0 | date,
data = clean_base, subset = year<=2007)
R7 <- felm(lspread ~ lriskaversion + lus10y + R_Y + PuD_Y + PrD_Y
| country_name_wb| 0 | date,
data = clean_base, subset = year<=2007)
```
```{r Regressions - After the GFC, echo=FALSE, message=FALSE, warning=FALSE, , echo=FALSE}
# AFTER THE GFC:
R8 <- felm(lspread ~ lriskaversion + lrating + lus10y + R_Y + PuD_Y + PrD_Y
| country_name_wb| 0 | date,
data = clean_base, subset = year>=2010)
R9 <- felm(lrating ~ lriskaversion + lus10y + R_Y + PuD_Y + PrD_Y
| country_name_wb| 0 | date,
data = clean_base, subset = year>=2010)
R10 <- felm(lspread ~ lriskaversion + lus10y + R_Y + PuD_Y + PrD_Y
| country_name_wb| 0 | date,
data = clean_base, subset = year>=2010)
```
```{r Text Output Regressions full sample, message=FALSE, warning=FALSE, include=FALSE}
Regressions1 <- stargazer(R1, R2, R4, R3,
type="html",
dep.var.labels=c("log(spread)","log(rating)"),
covariate.labels=c("log(Risk Aversion)","log(Credit Rating)",
"log(International Rate)",
"Reserve Ratio","Sovereign Debt Ratio",
"Private Debt Ratio"),
out="outputs/Self_Insurance_Table1.html",
title="Elasticities of emerging market spreads to reserves and debt stocks: full sample",
no.space=FALSE,
digits=3,
align=TRUE)
```
```{r Text Output Regressions before GFC, message=FALSE, warning=FALSE, include=FALSE}
Regressions2 <- stargazer(R5, R7, R6,
type="html",
dep.var.labels=c("log(spread)","log(rating)"),
covariate.labels=c("log(Risk Aversion)","log(Credit Rating)",
"log(International Rate)",
"Reserve Ratio","Sovereign Debt Ratio",
"Private Debt Ratio"),
out="outputs/Self_Insurance_Table2.html",
title="Elasticities of emerging market spreads to reserves and debt stocks: Before International Financial Crisis",
no.space=FALSE,
digits=3,
align=TRUE)
```
```{r Text Output Regressions after GFC, message=FALSE, warning=FALSE, include=FALSE}
Regressions3 <- stargazer(R8, R10, R9,
type="html",
dep.var.labels=c("log(spread)","log(rating)"),
covariate.labels=c("log(Risk Aversion)","log(Credit Rating)",
"log(International Rate)",
"Reserve Ratio","Sovereign Debt Ratio",
"Private Debt Ratio"),
out="outputs/Self_Insurance_Table3.html",
title="Elasticities of emerging market spreads to reserves and debt stocks: After International Financial Crisis",
no.space=FALSE,
digits=3,
align=TRUE)
```
```{r wald test, include=FALSE}
test1 <- as.data.frame(t(round(waldtest(R1, ~PuD_Y - R_Y)[1:2],6)))
test2 <- as.data.frame(t(round(waldtest(R2, ~PuD_Y - R_Y)[1:2],6)))
test3 <- as.data.frame(t(round(waldtest(R3, ~PuD_Y - R_Y)[1:2],6)))
test4 <- as.data.frame(t(round(waldtest(R4, ~PuD_Y - R_Y)[1:2],6)))
test5 <- as.data.frame(t(round(waldtest(R5, ~PuD_Y - R_Y)[1:2],6)))
test6 <- as.data.frame(t(round(waldtest(R6, ~PuD_Y - R_Y)[1:2],6)))
test7 <- as.data.frame(t(round(waldtest(R7, ~PuD_Y - R_Y)[1:2],6)))
test8 <- as.data.frame(t(round(waldtest(R8, ~PuD_Y - R_Y)[1:2],6)))
test9 <- as.data.frame(t(round(waldtest(R9, ~PuD_Y - R_Y)[1:2],6)))
test10<- as.data.frame(t(round(waldtest(R10, ~PuD_Y - R_Y)[1:2],6)))
coef_test <- rbind(test1, test2,test3, test4, test5, test6, test7, test8,test9, test10)
coef_names <- c("Full sample, Debt Public and Private",
"Full sample, Public Debt only",
"Full sample, Effect on Rating",
"Full sample, without Rating",
"Before GFC, Debt Public and Private",
"Before GFC, Effect on Rating",
"Before GFC, without Rating",
"After GFC, Debt Public and Private",
"After GFC, Effect on Rating",
"After GFC, without Rating")
coef_test$model <- coef_names
coef_test <- coef_test[c(3,1,2)]
tabla_coef <- kable(coef_test,
col.names = c("Regression Model", "p-value", "Chi2"),
caption="Wald test for Public Debt and Reserves") %>%
kable_styling(bootstrap_options = c("striped", "hover", "responsive"),
full_width = F,
position = "center")
print(tabla_coef)
```
```{r avplots, echo=FALSE}
R1.test <- lm(lspread ~ lriskaversion + lrating + lus10y + R_Y + PuD_Y + PrD_Y + country_name_wb, data = clean_base)
R2.test <- lm(lspread ~ lriskaversion + lrating + lus10y + R_Y + PuD_Y + country_name_wb , data = clean_base)
R3.test <- lm(lrating ~ lriskaversion + lus10y + R_Y + PuD_Y + PrD_Y + country_name_wb , data = clean_base)
R4.test <- lm(lspread ~ lriskaversion + lus10y + R_Y + PuD_Y + PrD_Y + country_name_wb , data = clean_base)
R5.test <- lm(lspread ~ lriskaversion + lrating + lus10y + R_Y + PuD_Y + PrD_Y + country_name_wb , data = clean_base, subset = year<=2007)
R6.test <- lm(lrating ~ lriskaversion + lus10y + R_Y + PuD_Y + PrD_Y + country_name_wb , data = clean_base, subset = year<=2007)
R7.test <- lm(lspread ~ lriskaversion + lus10y + R_Y + PuD_Y + PrD_Y + country_name_wb , data = clean_base, subset = year<=2007)
R8.test <- lm(lspread ~ lriskaversion + lrating + lus10y + R_Y + PuD_Y + PrD_Y + country_name_wb , data = clean_base, subset = year>=2010)
R9.test <- lm(lrating ~ lriskaversion + lus10y + R_Y + PuD_Y + PrD_Y + country_name_wb, data = clean_base, subset = year>=2010)
R10.test <- lm(lspread ~ lriskaversion + lus10y + R_Y + PuD_Y + PrD_Y + country_name_wb , data = clean_base, subset = year>=2010)
avPlots(R1.test, terms = ~ R_Y + PuD_Y + PrD_Y, col = "red", main = "Full Sample, Debt private and public")
avPlots(R2.test, terms = ~ R_Y + PuD_Y, col = "red", main = "Full Sample, public debt only")
avPlots(R3.test, terms = ~ R_Y + PuD_Y + PrD_Y, col = "red", main = "Full sample, Effect on Rating")
avPlots(R4.test, terms = ~ R_Y + PuD_Y + PrD_Y, col = "red", main = "Full sample, without Rating")
avPlots(R5.test, terms = ~ R_Y + PuD_Y + PrD_Y, col = "red", main = "Before IFC, Debt Public and Private")
avPlots(R6.test, terms = ~ R_Y + PuD_Y + PrD_Y, col = "red", main = "Before IFC, Effect on Rating")
avPlots(R7.test, terms = ~ R_Y + PuD_Y + PrD_Y, col = "red", main = "Before IFC, without Rating")
avPlots(R8.test, terms = ~ R_Y + PuD_Y + PrD_Y, col = "red", main = "After IFC, Debt Public and Private")
avPlots(R9.test, terms = ~ R_Y + PuD_Y + PrD_Y, col = "red", main = "After IFC, Effect on Rating")
avPlots(R10.test, terms = ~ R_Y + PuD_Y + PrD_Y, col = "red", main = "After IFC, without Rating")
```