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

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  • Christian Pfarr
  • Andreas Schmid
  • Udo Schneider

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.

Suggested Citation

  • Christian Pfarr & Andreas Schmid & Udo Schneider, 2010. "Estimating ordered categorical variables using panel data: a generalized ordered probit model with an autofit procedure," EERI Research Paper Series EERI_RP_2010_43, Economics and Econometrics Research Institute (EERI), Brussels.
    • Handle: RePEc:eei:rpaper:eeri_rp_2010_43
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    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software
    • I10 - Health, Education, and Welfare - - Health - - - General

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