Monte Carlo simulations using Stata
Overview
A Monte Carlo simulation (MCS) of an estimator approximates the sampling distribution of an estimator by simulation methods for a particular data-generating process (DGP) and sample size. I use an MCS to learn how well estimation techniques perform for specific DGPs. In this post, I show how to perform an MCS study of an estimator in Stata and how to interpret the results.
Large-sample theory tells us that the sample average is a good estimator for the mean when the true DGP is a random sample from a \(\chi^2\) distribution with 1 degree of freedom, denoted by \(\chi^2(1)\). But a friend of mine claims this estimator will not work well for this DGP because the \(\chi^2(1)\) distribution will produce outliers. In this post, I use an MCS to see if the large-sample theory works well for this DGP in a sample of 500 observations. Read more…