In addition to data, analysts often have available to them useful auxiliary information about inputs into their model for example, knowledge that high prices typically decrease demand or that sunny weather increases foot traffic at outdoor shopping malls. If used correctly and incorporated carefully into the analysis, the auxiliary information can significantly improve the quality of the analysis. But this information is often ignored. Bayesian analysis provides a principled means of incorporating this information into the model through the prior distribution, but it does not provide a road map for translating auxiliary information into a useful prior.
This 20-minute video from SAS’ Matthew Simpson reviews the basics of Bayesian analysis and provides a framework for turning auxiliary information into prior distributions for parameters in your model by using SAS® Econometrics software. It discusses common pitfalls and gives several examples of how to use the framework.
01:25 – The Bayesian story
03:07 – How to think about the prior
06:20 – Examples
07:28 – Tricks
14:40 – Informative priors
16:39 - Summary
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