英文:
Diff in Diff with panel dataset on R
问题
我有一个面板数据集，我想进行差异分析。现在这是我的回归模型：
fit3 <- glm(df$empstat ~ factor(year) + factor(stateicp) + migrant_category + treated*post + treated*migrant_category+ post*migrant_category + treated*post*migrant_category + race + educ + age +marst, data = df, weights = perwt, family = 'gaussian')
但这会让R假定每个观察都是相互独立的吗？如果是的话，我应该怎么做才能让R意识到这是一个面板数据集？
英文:
I have a panel dataset that I'd like to conduct diff in diff on. Right now this is my regression:
fit3 <- glm(df$empstat ~ factor(year) + factor(stateicp) + migrant_category + treated*post + treated*migrant_category+ post*migrant_category + treated*post*migrant_category + race + educ + age +marst, data = df, weights = perwt, family = 'gaussian')
but will this make R assume that each observation is independent of each other? If yes, what should I do to make R realize that this is a panel data?
答案1
得分: 0
如果您对固定效应模型和差异和差异感兴趣，请使用 plm 软件包。以下是来自Christopher Zorn的示例：
# 面板数据WDI<-read_csv("https://github.com/PrisonRodeo/GSERM-Ljubljana-APD-git/raw/main/Data/WDI3.csv")# 添加“冷战”变量：WDI$ColdWar <- with(WDI, ifelse(Year < 1990, 1, 0))# 保留一个数值年份变量（用于 -panelAR-）：WDI$YearNumeric <- WDI$Year# 将数据转换为面板数据框：WDI <- pdata.frame(WDI, index = c("ISO3", "Year"))# 仅提取那些在观察期间的某个时刻实施有薪育儿假政策的国家：WDI <- WDI %>%group_by(ISO3) %>%filter(any(PaidParentalLeave == 1))# 创建更好的趋势变量：WDI$Time <- WDI$YearNumeric - 1950# 固定效应模型...fe1 <- plm(ChildMortality ~ PaidParentalLeave + Time +PaidParentalLeave * Time, data = WDI,effect = "individual", model = "within")fe2 <- plm(ChildMortality ~ PaidParentalLeave + Time +PaidParentalLeave * Time + log(GDPPerCapita) +log(NetAidReceived) + GovtExpenditures,data = WDI, effect = "individual", model = "within")fe3 <- plm(ChildMortality ~ PaidParentalLeave + Time +PaidParentalLeave * Time, data = WDI,effect = "twoway", model = "within")fe4 <- plm(ChildMortality ~ PaidParentalLeave + Time +PaidParentalLeave * Time + log(GDPPerCapita) +log(NetAidReceived) + GovtExpenditures,data = WDI, effect = "twoway", model = "within")# 表格时间stargazer(fe1, fe2, fe3, fe4,title = "DiD Models of log(Child Mortality)",column.separate = c(1, 1, 1), align = TRUE,dep.var.labels.include = FALSE,dep.var.caption = "",covariate.labels = c("Paid Parental Leave", "Time (1950=0)","Paid Parental Leave x Time","ln(GDP Per Capita)","ln(Net Aid Received)","Government Expenditures"),header = FALSE, model.names = FALSE,model.numbers = FALSE, multicolumn = FALSE,object.names = TRUE, notes.label = "",column.sep.width = "-15pt",omit.stat = c("f", "ser"), type = "text")
DiD模型的log(儿童死亡率)
fe1 fe2 fe3 fe4
Paid Parental Leave -15.500*** -26.200*** -12.500*** -17.300*
(2.420) (7.220) (2.960) (9.360)
Time (1950=0) -0.838*** -1.480***
(0.025) (0.094)
Paid Parental Leave x Time -7.110*** -4.910*
(2.290) (2.600)
ln(GDP Per Capita) -1.780*** -3.020***
(0.471) (0.552)
ln(Net Aid Received) 0.873*** 0.842***
(0.139) (0.146)
Government Expenditures 0.310*** 0.524*** 0.247*** 0.319*
(0.044) (0.128) (0.056) (0.169)
Observations 2,360 622 2,360 622
R2 0.496 0.717 0.009 0.143
Adjusted R2 0.485 0.701 -0.035 0.014
*p<0.1; **p<0.05; ***p<0.01
<details><summary>英文:</summary>If you are interested in fixed effects models and difference in difference, use the `plm` package. Here is an example from Christopher Zorn:# Panel dataWDI<-read_csv("https://github.com/PrisonRodeo/GSERM-Ljubljana-APD-git/raw/main/Data/WDI3.csv")# Add a "Cold War" variable:WDI$ColdWar <- with(WDI,ifelse(Year<1990,1,0))# Keep a numeric year variable (for -panelAR-):WDI$YearNumeric<-WDI$Year# Make the data a panel dataframe:WDI<-pdata.frame(WDI,index=c("ISO3","Year"))# Pull out *only* those countries that, at some# point during the observed periods, instituted# a paid parental leave policy:WDI<-WDI %>% group_by(ISO3) %>%filter(any(PaidParentalLeave==1))# Create a better trend variable:WDI$Time<-WDI$YearNumeric-1950# FE models...fe1<-plm(ChildMortality~PaidParentalLeave+Time+PaidParentalLeave*Time,data=WDI,effect="individual",model="within")fe2<-plm(ChildMortality~PaidParentalLeave+Time+PaidParentalLeave*Time+log(GDPPerCapita)+log(NetAidReceived)+GovtExpenditures,data=WDI,effect="individual",model="within")fe3<-plm(ChildMortality~PaidParentalLeave+Time+PaidParentalLeave*Time,data=WDI,effect="twoway",model="within")fe4<-plm(ChildMortality~PaidParentalLeave+Time+PaidParentalLeave*Time+log(GDPPerCapita)+log(NetAidReceived)+GovtExpenditures,data=WDI,effect="twoway",model="within")# TABLE TIMEstargazer(fe1,fe2,fe3,fe4,title="DiD Models of log(Child Mortality)",column.separate=c(1,1,1),align=TRUE,dep.var.labels.include=FALSE,dep.var.caption="",covariate.labels=c("Paid Parental Leave","Time (1950=0)","Paid Parental Leave x Time","ln(GDP Per Capita)","ln(Net Aid Received)","Government Expenditures"),header=FALSE,model.names=FALSE,model.numbers=FALSE,multicolumn=FALSE,object.names=TRUE,notes.label="",column.sep.width="-15pt",omit.stat=c("f","ser"),type="text")DiD Models of log(Child Mortality)=====================================================================fe1 fe2 fe3 fe4---------------------------------------------------------------------Paid Parental Leave -15.500*** -26.200*** -12.500*** -17.300*(2.420) (7.220) (2.960) (9.360)Time (1950=0) -0.838*** -1.480***(0.025) (0.094)Paid Parental Leave x Time -7.110*** -4.910*(2.290) (2.600)ln(GDP Per Capita) -1.780*** -3.020***(0.471) (0.552)ln(Net Aid Received) 0.873*** 0.842***(0.139) (0.146)Government Expenditures 0.310*** 0.524*** 0.247*** 0.319*(0.044) (0.128) (0.056) (0.169)---------------------------------------------------------------------Observations 2,360 622 2,360 622R2 0.496 0.717 0.009 0.143Adjusted R2 0.485 0.701 -0.035 0.014=====================================================================*p<0.1; **p<0.05; ***p<0.01</details>
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