I’m going to create a correlation matrix here, since that seems to be easier for most people to think about. The first think you need to do to create your data set, is decide what you want the correlation or covariance matrix to look like. 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. The replication can be accomplished easily enough with a -forvalues- loop. 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. Then add in some random error, and estimate your statistic or model. Create a data set with a known correlation or covariance structure. 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. © David931 | Stock Free Images & Dreamstime Stock Photos
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