Merge pull request #7 from be-green/hotfix/line-up-castle-code

Update castle scripts

programs/castle_1.R

@@ -59,19 +59,7 @@ xvar <- c(
 
 law <- c("cdl")
 
-#--- Generating the formula
-formula1 <- as.formula(
-  paste("l_homicide ~ ",
-        paste(
-          paste(xvar, collapse = " + "),
-          paste(region, collapse = " + "),
-          paste(lintrend, collapse = " + "),
-          paste(law, collapse = " + "), sep = " + "),
-        "| year + sid | 0 | sid"
-  )
-)
-
-formula2 <- as.formula(
+dd_formula <- as.formula(
   paste("l_homicide ~ ",
         paste(
           paste(xvar, collapse = " + "),
@@ -82,10 +70,8 @@ formula2 <- as.formula(
   )
 )
 
-#Fixed effect regression using cdl as treatment variable 
-reg1 <- felm(formula1, weights = castle$popwt, data = castle)
-summary(reg1)
-
 #Fixed effect regression using post as treatment variable 
-reg2 <- felm(formula2, weights = castle$popwt, data = castle)
-summary(reg2)
+dd_reg <- felm(dd_formula, weights = castle$popwt, data = castle)
+summary(dd_reg)
+
+

programs/castle_2.R

@@ -19,7 +19,7 @@ castle <- castle %>%
     lag5 = case_when(time_til == 5 ~ 1, TRUE ~ 0)
   )
 
-formula3 <- as.formula(
+event_study_formula <- as.formula(
   paste("l_homicide ~ + ",
         paste(
           paste(region, collapse = " + "),
@@ -29,5 +29,5 @@ formula3 <- as.formula(
   ),
 )
 
-reg3 <- felm(formula3, weights = castle$popwt, data = castle)
-summary(reg3)
+event_study_reg <- felm(event_study_formula, weights = castle$popwt, data = castle)
+summary(event_study_reg)

programs/castle_3.R

@@ -10,8 +10,8 @@ plot_order <- c("lead9", "lead8", "lead7",
 # from the regression, label them accordingly
 # add a zero'th lag for plotting purposes
 leadslags_plot <- tibble(
-  sd = c(reg3$cse[plot_order], 0),
-  mean = c(coef(reg3)[plot_order], 0),
+  sd = c(event_study_reg$cse[plot_order], 0),
+  mean = c(coef(event_study_reg)[plot_order], 0),
   label = c(-9,-8,-7,-6, -5, -4, -3, -2, -1, 1,2,3,4,5, 0)
 )
 

programs/castle_5.R

@@ -28,12 +28,12 @@ bacon_plot <- ggplot(data = df_bacon) +
 bacon_plot
 
 # create formula
-formula4 <- as.formula(
+bacon_dd_formula <- as.formula(
   'l_homicide ~ post | year + sid | 0 | sid')
 
 # Simple diff-in-diff regression
-reg4 <- felm(formula = formula4, data = castle)
-summary(reg4)
+bacon_dd_reg <- felm(formula = bacon_dd_formula, data = castle)
+summary(bacon_dd_reg)
 
 # Note that the estimate from earlier equals the 
 # coefficient on post