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Consider a (stationary) autoregressive process, say of order 2,
for some white noise with variance . Here is a code to generate such a process,
> phi1=.5
> phi2=-.4
> sigma=1.5
> set.seed(1)
> n=240
> WN=rnorm(n,sd=sigma)
> ...

The Gaussian vector is extremely interesting since it remains Gaussian when conditioning. More precisely, if is a Gaussian random vector, then the conditional distribution of is also Gaussian. Further, it is possible to derive explicitly the cova...

Update: I have added some functionality to my ivregress() command. Check out my newer post here.After I posted my last video tutorial on how to use my IV regression function, I received a comment asking why I didn't write the command a different way t...

Update: I have replaced this video tutorial with a video tutorial on a newer, easier to use IV regression command. Check out that command here.In this video, I show how to use my instrumental variables function in R, ivreg(), along with its companion ...

Here is my code from a previous post that performs IV regression. This may be easier to copy into an R script. I will post a video tutorial using this code shortly.

Here's a video tutorial where I demonstrate an answer to this question.This video is designed to instill a baseline level of practical knowledge. There is more to how R treats factors in regression models. An interested reader should Google "R contra...

I created another video tutorial on R. This time, I discuss R's lm() command and how to use it for a variety of standard applications.Here is the code that goes with the video:Enjoy!