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Creating synthetic discrete-response regression models

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  • Joseph M. Hilbe

    (Arizona State University)

Abstract

The development and use of synthetic regression models has proven to assist statisticians in better understanding bias in data, as well as how to best interpret various statistics associated with a modeling situation. In this article, I present code that can be easily amended for the creation of synthetic binomial, count, and categorical response models. Parameters may be assigned to any num- ber of predictors (which are shown as continuous, binary, or categorical), negative binomial heterogeneity parameters may be assigned, and the number of levels or cut points and values may be specified for ordered and unordered categorical re- sponse models. I also demonstrate how to introduce an offset into synthetic data and how to test synthetic models using Monte Carlo simulation. Finally, I intro- duce code for constructing a synthetic NB2-logit hurdle model. Copyright 2010 by StataCorp LP.

Suggested Citation

  • Joseph M. Hilbe, 2010. "Creating synthetic discrete-response regression models," Stata Journal, StataCorp LLC, vol. 10(1), pages 104-124, March.
    • Handle: RePEc:tsj:stataj:v:10:y:2010:i:1:p:104-124
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    References listed on IDEAS

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    1. J. Scott Long & Jeremy Freese, 2006. "Regression Models for Categorical Dependent Variables using Stata, 2nd Edition," Stata Press books, StataCorp LLC, edition 2, number long2, March.
    2. Rainer Winkelmann, 2008. "Econometric Analysis of Count Data," Springer Books, Springer, edition 0, number 978-3-540-78389-3, January.
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