Monte Carlo simulations are most commonly used to understand the properties of a particular statistic such as the mean, or an estimator like maximum likelihood (ML) regression methods.
The principal is straight forward. Create a data set with a known correlation or covariance structure. Then add in some random error, and estimate your statistic or model.
Replicate this process 1,000 or 10,000 times – collecting the relevant information from each trial – and you’ll have a nice sampling distribution with which to evaluate the properties of your model or statistic.
The replication can be accomplished easily enough with a -forvalues- loop.
In this article, you’ll find out how to accomplish the other part of the task: creating a data set with a known correlation structure. Continue reading →
It doesn’t take long for new analysts to learn that copying and pasting code really speeds up the time needed to complete any job. This seems to be especially true when you need to create groups of new variables, or when performing the same transformation to a set of fields.
The reality is that copying and pasting code in these instances is actually the long way of accomplishing a task. Sure, the code will be easy to read. But you could complete the same tasks in a fraction of the time.
Fortunately, Stata has a set of built-in tools to make this process easier.
This article will show you how to use the -forvalues- command in Stata in order to automate repetitive tasks. Learning how to use this tool will help make your data analysis code cleaner, shorter, and faster to write. Continue reading →