Hi,
how to estimate a the following model in R:
y(t)=beta0+beta1*x1(t)+beta2*x2(t)+...+beta5*x5(t)+beta6*y(t-1)+beta7*y(t-2)+beta8*y(t-3)
1) using "lm" :
dates <- as.Date(data.df[,1])
selection<-which(dates>=as.Date("1986-1-1") &
dates<=as.Date("2007-12-31"))
dep <- ts(data.df[selection,c("dep")])
indep.ret1 <- ts(data.df[selection,c("RET1")])
indep.ret2 <- ts(data.df[selection,c("RET2")])
indep.ret3 <- ts(data.df[selection,c("RET3")])
indep.ret4 <- ts(data.df[selection,c("RET4")])
indep.ret5 <- ts(data.df[selection,c("RET5")])
d<-ts.union(dep,indep.ret1,indep.ret2,indep.ret3,indep.ret4,indep.ret5,dep.lag1=lag(dep,-1),dep.lag2=lag(dep,-2),dep.lag3=lag(dep,-3))
fit1 <-
lm(dep~indep.ret1+indep.ret2+indep.ret3+indep.ret4+indep.ret5+dep.lag1+dep.lag2+dep.lag3,data=d)
summary(fit1)
#coeftest(fit1,vcov=NeweyWest)
2) using armaFit:
fit2<-armaFit(dep~ar(3),xreg=ts(data.df[selection,c("RET1","RET2","RET3","RET4","RET5")]),data=ts(data.df[selection,-1]))
summary(fit2)
The results of 1) and 2) are completely different. Does anybody have an
explanation for this?
The dependent and some independent variables are autocorrelated because of
overlapping observations (but do not posess a unit root). Therefore I have
added lagged dependent variables as additional regressors to resolve the
problem of autocorrelation in the dependent variables. To account for residual
autocorrelation in the residuals I want to use the procedure of Newey West. Is
this idea absolute nonsense?
Kind regards,
Daphne
[[alternative HTML version deleted]]
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
