Fitting heterogeneous choice models with oglm
When a binary or ordinal regression model incorrectly assumes that er- ror variances are the same for all cases, the standard errors are wrong and (unlike ordinary least squares regression) the parameter estimates are biased. Hetero- geneous choice models (also known as location–scale models or heteroskedastic ordered models) explicitly specify the determinants of heteroskedasticity in an at- tempt to correct for it. Such models are also useful when the variance itself is of substantive interest. This article illustrates how the author’s Stata program oglm (ordinal generalized linear models) can be used to fit heterogeneous choice and related models. It shows that two other models that have appeared in the liter- ature (Allison’s model for group comparisons and Hauser and Andrew’s logistic response model with proportionality constraints) are special cases of a heteroge- neous choice model and alternative parameterizations of it. The article further argues that heterogeneous choice models may sometimes be an attractive alterna- tive to other ordinal regression models, such as the generalized ordered logit model fit by gologit2. Finally, the article offers guidelines on how to interpret, test, and modify heterogeneous choice models. Copyright 2010 by StataCorp LP.
Volume (Year): 10 (2010)
Issue (Month): 4 (December)
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References listed on IDEAS
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- Richard Williams, 2006. "Generalized ordered logit/partial proportional odds models for ordinal dependent variables," Stata Journal, StataCorp LP, vol. 6(1), pages 58-82, March.
- Paul D. Allison, 1999. "Comparing Logit and Probit Coefficients Across Groups," Sociological Methods & Research, , vol. 28(2), pages 186-208, November.
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- J. Scott Long & Jeremy Freese, 2006. "Regression Models for Categorical Dependent Variables using Stata, 2nd Edition," Stata Press books, StataCorp LP, edition 2, number long2, September.
- Ben Jann, 2005. "Making regression tables from stored estimates," Stata Journal, StataCorp LP, vol. 5(3), pages 288-308, September.
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