Estimating ordered categorical variables using panel data: a generalized ordered probit model with an autofit procedure

Pfarr, Christian and Schmid, Andreas and Schneider, Udo (2010): Estimating ordered categorical variables using panel data: a generalized ordered probit model with an autofit procedure.

Abstract

Estimation procedures for ordered categories usually assume that the estimated coefficients of independent variables do not vary between the categories (parallel-lines assumption). This view neglects possible heterogeneous effects of some explaining factors. This paper describes the use of an autofit option for identifying variables that meet the parallel-lines assumption when estimating a random effects generalized ordered probit model. We combine the test procedure developed by Richard Williams (gologit2) with the random effects estimation command regoprob by Stefan Boes.

• Item Type: MPRA Paper
• Language: English
• Keywords: generalized ordered probit; panel data; autofit, self-assessed health
• Subjects: C - Mathematical and Quantitative Methods > C8 - Data Collection and Data Estimation Methodology ; Computer Programs > C87 - Econometric Software; C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C23 - Panel Data Models ; Spatio-temporal Models; C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C25 - Discrete Regression and Qualitative Choice Models ; Discrete Regressors ; Proportions ; Probabilities; I - Health, Education, and Welfare > I1 - Health > I10 - General

References

Boes, S. (2007), Three Essays on the Econometric Analysis of Discrete Dependent Variables, Universität Zürich, Zürich.

Boes, S. and Winkelmann, R. (2006), Ordered Response Models, in: Allgemeines Statisti-sches Archiv, 90, pp. 167–181.

Börsch-Supan, A., Coppola, M., Essig, L., Eymann, A. and Schunk, D. (2008), The German SAVE Study - Design and Results, mea studies 06, Mannheim Research Institute for the Economics of Aging, Mannheim.

Frechette, G. R. (2001), sg158: Random-Effects Ordered Probit, in: Stata Technical Bulletin, 59, pp. 23–27.

Greene, W. H., Harris, M. N., Hollingsworth, B. and Maitra, P. (2008), A Bivariate Latent Class Correlated Generalized Ordered Probit Model with an Application to Mod-eling Observed Obesity Levels, Working Paper, Nr. 08-18, New York University, Department of Economics, New York.

Greene, W. H. and Hensher, D. A. (2010), Modeling ordered choices, A primer, Cambridge University Press, Cambridge.

Long, J. S. (1997), Regression models for categorical and limited dependent variables, Sage Publ., Thousand Oaks, Calif.

Pfarr, C., Schmid, A. and Schneider, U. (2010), REGOPROB2: Stata module to estimate ran-dom effects generalized ordered probit models (update), Statistical Software Components, Boston College Department of Economics.

Pfarr, C., Schneider, B. S., Schneider, U. and Ulrich, V. (2010), Self-assessed health, gender differences and reporting heterogeneity: empirical evidence using multiple im-puted data, Discussion Paper, Nr. 03-10, University of Bayreuth, Department of Law and Economics, Bayreuth.

Pudney, S. and Shields, M. (2000), Gender, Race, Pay and Promotion in the British Nursing Profession, Estimation of a Generalized Ordered Probit Model, in: Journal of Ap-plied Econometrics, 15(4), pp. 367–399.

Williams, R. (2006), Generalized ordered logit/partial proportional odss models for ordinal dependent variables, in: Stata Journal, 6(1), pp. 58–82.