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

  • Pfarr, Christian
  • Schmid, Andreas
  • Schneider, Udo

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.

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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 24181.

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Date of creation: 10 Jun 2010
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Handle: RePEc:pra:mprapa:24181
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  1. Stefan Boes & Rainer Winkelmann, 2006. "Ordered response models," AStA Advances in Statistical Analysis, Springer, vol. 90(1), pages 167-181, March.
  2. 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.
  3. Guillaume R. Frechette, 2001. "Random-effects ordered probit," Stata Technical Bulletin, StataCorp LP, vol. 10(59).
  4. Greene,William H. & Hensher,David A., 2010. "Modeling Ordered Choices," Cambridge Books, Cambridge University Press, number 9780521142373.
  5. Stephen Pudney & Michael Shields, . "Gender, Race, Pay and Promotion in the British Nursing Profession: Estimation of a Generalised Ordered Probit Model," Discussion Papers in Public Sector Economics 97/4, Department of Economics, University of Leicester.
  6. Greene,William H. & Hensher,David A., 2010. "Modeling Ordered Choices," Cambridge Books, Cambridge University Press, number 9780521194204.
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