Thursday, December 19, 2013

Maximum Likelihood Estimation in EViews

This post is all about estimating regression models by the method of Maximum Likelihood, using EViews. It's based on a lab. class from one of my grad. econometrics courses.

We don't go through all of the material below in class - PART 3 is left as an exercise for the students to pursue in their own time.

The data and the EViews workfile can be found on the data page and the code page for this blog.

The purpose of this lab. exercise is to help the students to learn how to use EViews to estimate the parameters of a regression model by Maximum Likelihood, when the model is of some non-standard type. Specifically, find lout how to estimate models of types that are not “built in” as a standard option in EViews. This involves setting up the log-likelihood function for the model, based on the assumption of independent observations; and then maximizing this function numerically with respect to the unknown parameters. 

First, to introduce the concepts and commands that are involved, we consider the standard  linear multiple regression model with normal errors, for which we know that the MLE of the coefficient vector is just the same as the OLS estimator. This will give us a “bench-mark” against which to check our understanding of what is going on. Then we can move on to some more general models.