Estimating ordered categorical variables using panel data: a generalized ordered probit model with an autofit procedure
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.
|Date of creation:||10 Jun 2010|
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- Stephen Pudney & Michael Shields, .
"Gender, Race, Pay and Promotion in the British Nursing Profession: Estimation of a Generalised Ordered Probit Model,"
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- Stephen Pudney & Michael Shields, . "Gender, Race, Pay and Promotion in the British Nursing Profession Estimation of a Generalised Ordered ProbitModel," Discussion Papers in Economics 97/4, Department of Economics, University of Leicester.
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- William H. Greene & Mark N. Harris & Bruce Hollingworth & Pushkar Maitra, 2008. "A Bivariate Latent Class Correlated Generalized Ordered Probit Model with an Application to Modeling Observed Obesity Levels," Working Papers 08-18, New York University, Leonard N. Stern School of Business, Department of Economics.
- Stefan Boes & Rainer Winkelmann, 2006.
"Ordered response models,"
AStA Advances in Statistical Analysis,
Springer;German Statistical Society, vol. 90(1), pages 167-181, March.
- Guillaume R. Frechette, 2001. "Random-effects ordered probit," Stata Technical Bulletin, StataCorp LP, vol. 10(59).
- Christian Pfarr & Andreas Schmid & Udo Schneider, 2010. "REGOPROB2: Stata module to estimate random effects generalized ordered probit models (update)," Statistical Software Components S457153, Boston College Department of Economics.
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