R CODES FOR NONPARAMETRIC TIME SERIES MODEL
By Dr JOHN MUTEBA MWAMBA
DEPARTMENT OF ECONOMICS AND ECONOMETRICS
UNIVERSITY OF JOHANNESBURG
>library(Rcmdr)##import data via Rcmdr package; the data is NOT as.timeSeries
>library(np)#
>fhat<-npcdens(y~x1,data=Dataset)#visualize the kernel function of two observed
##variables y and x;
>summary(fhat)
>plot(fhat,view=”fixed”,main=””,theta=300,phi=50)#Retype the inverted commas
##View the cumulative function of the kernel;
>plot(fhat, cdf=TRUE,view=”fixed”,main=””,theta=300,phi=50)
##NONPARAMETRIC REGRESSION: KERNEL REGRESSION
## your data=Dataset contains dependent variable y, and independent variables x1, x2,
##x3 and x4. We divide this sample data into two subsamples: the in-sample with size
##n1 and the out-sample with size n2; for example n1=400 and n2=21, the total sample
##size is n=n1+n2=421.
>set.seed(123)
#creating the in-sample and out-sample data
>n1=400
>n2=21
>whol<-sample(1:nrow(Dataset))
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>insample1<-Dataset[whol[1:n1],]
>outsample<-Dataset[whol[n1+1:nrow(Dataset)],] ;#input the sum n1+1 not the formula
#by default, the npreg will result in Nadaraya-Watson estimators in this case type:
>modelnp<-npreg(y~x1+x2+x3+x4,data=Dataset) #if you want the local linear then:
#get the data-driven bandwidth
##>bwselect<-npregbw(y~x1+x2+x3+x4, regtype=”ll”,bwmethod=”cv.aic”,data=Dataset)
##> modelnp<-npreg(bws=bwselect)
##>summary(modelnp)
>summary(modelnp) #to see the regression results
>plot(modelnp)
##test of significance
>npsigtest(modelnp)
##FORECAST
>focastnp<-predict(modelnp,newdata=outsample)
>pmse1<-mean((outsample$y-focastnp)^2)
>pmse1 ##this is the predicted mean square error of the nonparametric model
##To compare the nonparametric model with (linear) parametric model
##First compute the linear model
>modellin<-lm(y~x1+x2+x3+x4,data=Dataset)
>summary(modellin)
##Plot the linear model (modellin), but not important
>plot(modellin)
##Now compute the forecast for the linear model
>focastlin<-predict(modellin,newdata=outsample)
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>pmse2<-mean((outsample$y-focastlin)^2)
>pmse2 ##this is the predicted mean square error for the linear model
##NB: the best model will have the smallest predicted mean square error value
###DIEBOLD MARINO TEST (dm.test) for two econometric models; you must load the
##package “forecast”
>library(forecast)
##the dm.test need residuals from the two econometric models
>errorlin<-residuals(modellin)
>errornp<-residuals(modelnp)
> dm.test(errornp,errorlin,alternative=(”two.sided”,”less”,”greater”),h=1,power=2)
###Notice that alternative must be one of “two.sided” or “less” or “greater” and that h is the
##forecast horizon, h=1 means one step ahead forecast, h=2 means two step ahead forecast,
##h=3…
##NB: If you are not using out-sample forecasts then you can use the following code to obtain
dm.test:
> dm.test(residuals(modelnp),residuals(modellin),
alternative=(”two.sided”,”less”,”greater”),h=1,power=2)
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