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On the Returns to Occupational Qualification in Terms of Subjective and Objective Variables: A GEE-type Approach to the Estimation of Two-Equation Panel Models

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  • Martin Spieß

Abstract

This article proposes an estimation approach for panel models with mixed continuous and ordered categorical outcomes based on generalized estimating equations for the mean and pseudo-score equations for the covariance parameters. A numerical study suggests that efficiency can be gained as concerns the mean parameter estimators by using individual covariance matrices in the estimating equations for the mean parameters. The approach is applied to estimate the returns to occupational qualification in terms of income and perceived job security in a nine-year period based on the German Socio-Economic Panel (SOEP). To compensate for missing data, a combined multiple imputation/weighting approach is adopted.

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  • Martin Spieß, 2006. "On the Returns to Occupational Qualification in Terms of Subjective and Objective Variables: A GEE-type Approach to the Estimation of Two-Equation Panel Models," Discussion Papers of DIW Berlin 564, DIW Berlin, German Institute for Economic Research.
    • Handle: RePEc:diw:diwwpp:dp564
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    1. Beth Reboussin & Kung-Yee Liang, 1998. "An estimating equations approach for the LISCOMP model," Psychometrika, Springer;The Psychometric Society, vol. 63(2), pages 165-182, June.
    2. Spiess, Martin & Hamerle, Alfred, 2000. "A comparison of different methods for the estimation of regression models with correlated binary responses," Computational Statistics & Data Analysis, Elsevier, vol. 33(4), pages 439-455, June.
    3. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, April.
    4. Bengt Muthén & Albert Satorra, 1995. "Technical aspects of Muthén's liscomp approach to estimation of latent variable relations with a comprehensive measurement model," Psychometrika, Springer;The Psychometric Society, vol. 60(4), pages 489-503, December.
    5. Mary Dupuis Sammel & Louise M. Ryan & Julie M. Legler, 1997. "Latent Variable Models for Mixed Discrete and Continuous Outcomes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 59(3), pages 667-678.
    6. Gueorguieva R. V. & Agresti A., 2001. "A Correlated Probit Model for Joint Modeling of Clustered Binary and Continuous Responses," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1102-1112, September.
    7. D. B. Dunson, 2000. "Bayesian latent variable models for clustered mixed outcomes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(2), pages 355-366.
    8. Richard A. Easterlin (ed.), 2002. "Happiness in Economics," Books, Edward Elgar Publishing, number 2479.
    9. SOEP Group, 2001. "The German Socio-Economic Panel (GSOEP) after More than 15 Years: Overview," Vierteljahrshefte zur Wirtschaftsforschung / Quarterly Journal of Economic Research, DIW Berlin, German Institute for Economic Research, vol. 70(1), pages 7-14.
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    Keywords

    Generalized estimating equations; mean and covariance model; multiple imputation; pseudo-score equations; status inconsistency; weighting;
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