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Bayesian Inference in Regression Models with Ordinal Explanatory Variables

Author

Listed:
  • Karlsson, Sune

    (Örebro University School of Business)

  • Temesgen, Asrat

    (Örebro University School of Business)

Abstract

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.

Suggested Citation

  • Karlsson, Sune & Temesgen, Asrat, 2015. "Bayesian Inference in Regression Models with Ordinal Explanatory Variables," Working Papers 2015:9, Örebro University, School of Business.
    • Handle: RePEc:hhs:oruesi:2015_009
    as

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    References listed on IDEAS

    as
    1. 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.
    2. Terza, Joseph V., 1987. "Estimating linear models with ordinal qualitative regressors," Journal of Econometrics, Elsevier, vol. 34(3), pages 275-291, March.
    3. 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.
    4. 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.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Markov Chain Monte Carlo; latent variables; ordered probit; wage discrimination;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials

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