Unobserved Heterogeneity in the Binary Logit Model with Cross-Sectional Data and Short Panels: A Finite Mixture Approach
Anders Holm (),
Mads Meier Jæger and
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Mads Meier Jæger: Danish National Centre for Social Research, Copenhagen
Morten Pedersen: Department of Sociology, University of Copenhagen
No 2009-04, CAM Working Papers from University of Copenhagen. Department of Economics. Centre for Applied Microeconometrics
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.
Keywords: binary logit model; unobserved heterogeneity; latent classes; simulation (search for similar items in EconPapers)
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