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.
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Abstract
Estimation procedures for ordered categories usually assume that the estimated coefficients of independent variables do not vary between the categories (parallellines 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 parallellines 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 

Original Title:  Estimating ordered categorical variables using panel data: a generalized ordered probit model with an autofit procedure 
Language:  English 
Keywords:  generalized ordered probit; panel data; autofit, selfassessed 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 ; Spatiotemporal 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 
Item ID:  24181 
Depositing User:  Christian Pfarr 
Date Deposited:  02. Aug 2010 08:32 
Last Modified:  31. Dec 2015 00:06 
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 Statistisches Archiv, 90, pp. 167–181. BörschSupan, 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: RandomEffects 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 Modeling Observed Obesity Levels, Working Paper, Nr. 0818, 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 random 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), Selfassessed health, gender differences and reporting heterogeneity: empirical evidence using multiple imputed data, Discussion Paper, Nr. 0310, 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 Applied 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. 
URI:  https://mpra.ub.unimuenchen.de/id/eprint/24181 
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