v1.1.7

Normal approximation for CI (instead of percentile methods) to obtain symmetric CIs.
xuyiqing · Oct 30, 2020 · d0010b6 · d0010b6
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diff --git a/DESCRIPTION b/DESCRIPTION
@@ -1,8 +1,8 @@
 Package: gsynth
 Type: Package
 Title: Generalized Synthetic Control Method
-Version: 1.1.6
-Date: 2020-05-15
+Version: 1.1.7
+Date: 2020-10-29
 Author: Yiqing Xu, Licheng Liu
 Maintainer: Yiqing Xu <yiqingxu@ucsd.edu>
 Description: Provides causal inference with interactive fixed-effect models. It imputes counterfactuals for each treated unit using control group information based on a linear interactive fixed effects model that incorporates unit-specific intercepts interacted with time-varying coefficients. This method generalizes the synthetic control method to the case of multiple treated units and variable treatment periods, and improves efficiency and interpretability. This version supports unbalanced panels and implements the matrix completion method. Main reference: Yiqing Xu (2017) <doi:10.1017/pan.2016.2>.

diff --git a/R/core.R b/R/core.R
@@ -2849,80 +2849,60 @@ synth.boot<-function(Y,
         }
     }
 
+    # att by time
     if (inference == "jackknife") {
-
         att.j <- jackknifed(att, att.boot, alpha)
         est.att <- cbind(att, att.j$se, att.j$CI.l, att.j$CI.u, att.j$P, ntreated)
-
     } else {
-        conf.lvl.lb <- alpha/2 
-        conf.lvl.ub <- 1 - alpha/2
-
-        CI.att <- t(apply(att.boot, 1, function(vec) 
-            quantile(vec,c(conf.lvl.lb, conf.lvl.ub), na.rm=TRUE)))
         se.att <- apply(att.boot, 1, function(vec) sd(vec, na.rm=TRUE))
-        pvalue.att <- apply(att.boot, 1, get.pvalue)
-
+        CI.att <- cbind(att - se.att * qnorm(1-alpha/2), att + se.att * qnorm(1-alpha/2)) # normal approximation
+        pvalue.att <- (1-pnorm(abs(att/se.att)))*2
         est.att <- cbind(att, se.att, CI.att, pvalue.att, ntreated)
-
     }
-    colnames(est.att) <- c("ATT", "S.E.", "CI.lower", "CI.upper",
-                               "p.value", "n.Treated")
+    colnames(est.att) <- c("ATT", "S.E.", "CI.lower", "CI.upper","p.value", "n.Treated")
 
 
     ## average (over time) ATT
     if (inference == "jackknife") {
-        ## average (over time) ATT
         att.avg.j <- jackknifed(att.avg, att.avg.boot, alpha)
         est.avg <- t(as.matrix(c(att.avg, att.avg.j$se, att.avg.j$CI.l, att.avg.j$CI.u, att.avg.j$P)))
-
     } else {
-        CI.avg <- quantile(att.avg.boot, c(conf.lvl.lb, conf.lvl.ub), na.rm=TRUE)
         se.avg <- sd(att.avg.boot, na.rm=TRUE)
-        pvalue.avg <- get.pvalue(att.avg.boot)
+        CI.avg <- c(att.avg - se.avg * qnorm(1-alpha/2), att.avg + se.avg * qnorm(1-alpha/2))
+        pvalue.avg <- (1-pnorm(abs(att.avg/se.avg)))*2
         est.avg <- t(as.matrix(c(att.avg, se.avg, CI.avg, pvalue.avg)))
-
-    }
-
-    colnames(est.avg) <- c("ATT.avg", "S.E.", "CI.lower", "CI.upper", "p.value")
-    rownames(est.avg) <- ""
+    }    
+    colnames(est.avg) <- c("Estimate", "S.E.", "CI.lower", "CI.upper", "p.value")
+    rownames(est.avg) <- "ATT.avg"
 
     ## individual effects
     if (inference == "parametric") {
-        CI.ind <- apply(eff.boot,c(1,2),function(vec)
-            quantile(vec,c(conf.lvl.lb, conf.lvl.ub), na.rm=TRUE)) ## 2*T*Ntr
         est.ind <- array(NA,dim=c(TT, 5, Ntr)) ## eff, se, CI.lower, CI.upper
         est.ind[,1,] <- eff
         est.ind[,2,] <- apply(eff.boot,c(1,2),sd)
-        est.ind[,3,] <- CI.ind[1,,]
-        est.ind[,4,] <- CI.ind[2,,]
-        est.ind[,5,] <- apply(eff.boot,c(1,2),get.pvalue)
-
+        est.ind[,3,] <- est.ind[,1,] - est.ind[,2,] * qnorm(1-alpha/2)
+        est.ind[,4,] <- est.ind[,1,] + est.ind[,2,] * qnorm(1-alpha/2)
+        est.ind[,5,] <- (1-pnorm(abs(est.ind[,1,]/est.ind[,2,])))*2
         dimnames(est.ind)[[1]] <- rownames(est.att)
-        dimnames(est.ind)[[2]] <- c("EFF", "S.E.", "CI.lower", "CI.upper", "p.value")
+        dimnames(est.ind)[[2]] <- c("Eff", "S.E.", "CI.lower", "CI.upper", "p.value")
     }
-
     colboot <- sapply(1:nboots, function(i){paste("boot",i,sep="")})
 
 
-    ## regression coefficents
+    ## regression coefficients
     if (p>0) {
         if (inference == "jackknife") {
             beta.j <- jackknifed(beta, beta.boot, alpha)
             est.beta <- cbind(beta, beta.j$se, beta.j$CI.l, beta.j$CI.u, beta.j$P)
         } else {
-            CI.beta<-t(apply(beta.boot, 1, function(vec)
-                quantile(vec,c(conf.lvl.lb, conf.lvl.ub), na.rm=TRUE)))
             se.beta<-apply(beta.boot, 1, function(vec)sd(vec,na.rm=TRUE))
-            pvalue.beta <- apply(beta.boot, 1, get.pvalue)
-            ## beta[na.pos] <- NA
+            CI.beta<-cbind(c(beta) - se.beta * qnorm(1-alpha/2), c(beta) + se.beta * qnorm(1-alpha/2))
+            pvalue.beta <- (1-pnorm(abs(beta/se.beta)))*2
             est.beta<-cbind(beta, se.beta, CI.beta, pvalue.beta)
-        }
-
+        }        
         colnames(est.beta)<-c("beta", "S.E.", "CI.lower", "CI.upper", "p.value")
         colnames(beta.boot) <- colboot
     }
-
     rownames(att.boot) <- rownames(est.att)
     colnames(att.boot) <- colboot