District-month predictions of violence in Afghanistan

gistfile1.r

##programmer: JEY
##2/28/2013
##District-month forecasts
##note: this takes about 5 minutes to run.  It throws lots of errors due to many all zero time series, but it will eventually complete and build predictions

rm(list=ls())
library(foreign)
library(forecast)
library(sos)
data<-read.dta("raw_AFG.dta")

month=0
error.naive=0
error.arfima=0
start=580
end=627
for (k in start:end){
  month<-k	
	error<-matrix(data=0, nrow=317, ncol=3)
	colnames(error)<-c("true", "naive", "arfima")
		for (i in 1:317){
			test<-subset(data, newid==i &monthly<month)
			a<-test$count
			unif<-runif(length(a), min=0, max=.1)
			a<-a+unif #this minor addition allows the arfima to converge
			fit<-arfima(a)
			y_hat<-forecast(fit, h=1)
			y<-as.vector(y_hat$mean)
			error[i,3]<-y
			true<-subset(data, newid==i &monthly==month) #actual leve of violence
    		error[i,1]<-true$count
    		naive<-subset(data, newid==i &monthly==(month-1)) #naive predict of t-1
    		error[i,2]<-naive$count
		}
	error<-round(error, digits = 0)
	error.naive[k-(start-1)]<-(sum(abs(error[,2]-error[,1])))/317
	error.arfima[k-(start-1)]<-(sum(abs(error[,3]-error[,1])))/317
}

error.naive
error.arfima
compare<-cbind(error.naive, error.arfima)
dif<-as.matrix(error.naive-error.arfima)
results<-cbind(compare, dif)
colnames(results)<-c("error.naive", "error.arfima", "difference")
write.csv(results, "results_district.csv")