Bayesian Inference in Regression Models with Ordinal Explanatory Variables
This paper considers Bayesian inference procedures for regression models with ordinally observed explanatory variables. Taking advantage of a latent variable interpretation of the ordinally observed variable we develop an efficient Bayesian inference procedure that estimates the regression model of interest jointly with an auxiliary ordered probit model for the unobserved latent variable. The properties of the inference procedure and associated MCMC algorithm are assessed using simulated data. We illustrate our approach in an investigation of gender based wage discrimination in the Swedish labor market and find evidence of wage discrimination.
|Date of creation:||18 Sep 2015|
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- Breslaw, Jon A. & McIntosh, James, 1998. "Simulated latent variable estimation of models with ordered categorical data," Journal of Econometrics, Elsevier, vol. 87(1), pages 25-47, August.
- Oaxaca, Ronald, 1973. "Male-Female Wage Differentials in Urban Labor Markets," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 14(3), pages 693-709, October.
- Terza, Joseph V., 1987. "Estimating linear models with ordinal qualitative regressors," Journal of Econometrics, Elsevier, vol. 34(3), pages 275-291, March.
- Alan S. Blinder, 1973. "Wage Discrimination: Reduced Form and Structural Estimates," Journal of Human Resources, University of Wisconsin Press, vol. 8(4), pages 436-455.
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