I promised I'll update my blog, but I have been really really busy. Anyway, this post is not about the KLCI as promised(sorry to disappoint,I hardly had time for myself either), but rather, it is a loop for Q3.1 in Davidson and McKinnon's Econometric Theory and Methods. For all the poor souls who have just started learning R.
B1<-1
B2<-0.8
y0<-0
B1hat<-numeric(0)
B2hat<-numeric(0)
for(j in 1:100){
residualsj<-numeric(0)
residualsj<-rnorm(200,0,1)
ysimj<-y0
for(i in 1:200){
ynew<-B1+B2*ysimj[i]+residualsj[i]
ysimj<-c(ysimj,ynew)
}
OLSj<-lm(ysimj[2:201]~ysimj[1:200])
beta1j<-coefficients(OLSj)[1]
beta2j<-coefficients(OLSj)[2]
B1hat[j]<-beta1j
B2hat[j]<-beta2j
}
Time permitting I'll write an explanatory note with the #, although I hardly doubt it with midterms and a lot of other deadlines vying for whatever precious time I have. To change the sample size just change the numbers in the "i" part of the loop. To change the number of runs, just change the numbers in the j part of the loop.
Update:I made some errors in the previous post and those have since be corrected. Note that this script is meant to simulate an OLS drawing from 200 observations 100 times, and feel free to tweak around to adjust the trials and sample size.
This should also make it easier for you to attempt the last 2 questions. For information on how to extract vectors out from a data frame and then transforming it, I'd refer you to Mirza Trokic's website.
http://www.mirzatrokic.ca/aes.html
He has the most concise and helpful R guide that I have ever seen.
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