Archives for posts with tag: #monte carlo

Yesterday during a presentation I gave there were a few questions about the interpretation of p-values.  My opinion is that classical hypothesis testing confuses many people and there a few things that are counterintuitive. After the presentation, I corresponded with one of the participants and ended up creating a Monte Carlo simulation to help illustrate hypothesis testing and p-value interpretation. It seemed like a logical thing to turn into a blog post.

Peter Kennedy’s Guide to Econometrics has a great treatment of Monte Carlos and explains many topics in econometrics through the lens of Monte Carlos. I also had a time series professor who illustrated topics with Monte Carlos and I found that this approach helped solidify these topics in my mind.

Two things follow: a link to my Github with output from a Monte Carlo simulation that I ran in Stata and a write up based on my correspondence with the participant.

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Scaling or deflating variables is common in accounting and finance research. It is often done to mitigate heteroskedasticity or the influence of firm size on parameter estimates. However, using analytic results and Monte Carlo simulations we show that common forms of scaling induce substantial spurious correlation via biased parameter estimates. Researchers are typically better off dealing with both heteroskedasticity and the influence of large firms using techniques other than scaling.

The full paper is here: