Unobserved Heterogeneity in the Binary Logit Model with Cross-Sectional Data and Short Panels: A Finite Mixture Approach
This paper proposes a new approach to dealing with unobserved heterogeneity in applied research using the binary logit model with cross-sectional data and short panels. Unobserved heterogeneity is particularly important in non-linear regression models such as the binary logit model because, unlike in linear regression models, estimates of the effects of observed independent variables are biased even when omitted independent variables are uncorrelated with the observed independent variables. We propose an extension of the binary logit model based on a finite mixture approach in which we conceptualize the unobserved heterogeneity via latent classes. Simulation results show that our approach leads to considerably less bias in the estimated effects of the independent variables than the standard logit model. Furthermore, because identification of the unobserved heterogeneity is weak when the researcher has cross-sectional rather than panel data, we propose a simple approach that fixes latent class weights and improves identification and estimation. Finally, we illustrate the applicability of our new approach using Canadian survey data on public support for redistribution.
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- Mette Ejrnæs & Anders Holm, 2004. "Comparing Fixed Effects and Covariance Structure Estimators," CAM Working Papers 2004-02, University of Copenhagen. Department of Economics. Centre for Applied Microeconometrics.
- Bearse, Peter & Canals-Cerd , Jos & Rilstone, Paul, 2007. "Efficient Semiparametric Estimation Of Duration Models With Unobserved Heterogeneity," Econometric Theory, Cambridge University Press, vol. 23(02), pages 281-308, April.
- Holm, Anders, 2002. "The effect of training on search durations: a random effects approach," Labour Economics, Elsevier, vol. 9(3), pages 433-450, July.
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