library(openxlsx) library(stargazer) sample1<-read.xlsx("http://kanggc.iptime.org/data/quarter.xlsx") m1<-ts(sample1$m1, start=c(1986, 4), frequency=4) gdp<-ts(sample1$gdp, start=c(1986, 4), frequency=4) cpi<-ts(sample1$cpi, start=c(1986, 4), frequency=4) r<-ts(sample1$r, start=c(1986, 4), frequency=4) lm1=log(m1) lgdp=log(gdp) lcpi=log(cpi) ols1<-lm(lm1~lgdp+lcpi+r) summary(ols1) gdp_1=gdp[140]*1.0025 cpi_1=cpi[140]*1.0025 r_1=r[140]-0.5 scen1<-data.frame(lgdp=log(gdp_1), lcpi=log(cpi_1), r=r_1) p1<-predict(lm(lm1~lgdp+lcpi+r), scen1, se.fit=T) (lm1f<-p1$fit) (m1ff<-exp(lm1f)) pred.w.clim_scen1<-predict(lm(lm1~lgdp+lcpi+r), scen1, interval="confidence") pred.w.clim_scen1 pred.w.plim_scen1<-predict(lm(lm1~lgdp+lcpi+r), scen1, interval="prediction") pred.w.plim_scen1 gdp_2=gdp[140]*1.006 cpi_2=cpi[140]*1.006 r_2=r[140]-0.25 scen2<-data.frame(lgdp=log(176556.9), lcpi=log(116.2), r=3.18) predict(lm(lm1~lgdp+lcpi+r), scen2, se.fit=T) pred.w.clim_scen2<-predict(lm(lm1~lgdp+lcpi+r), scen2, interval="confidence") pred.w.clim_scen2 pred.w.plim_scen2<-predict(lm(lm1~lgdp+lcpi+r), scen2, interval="prediction") pred.w.plim_scen2