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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 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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  • Christian Pfarr & Andreas Schmid & Udo Schneider, 2011. "Estimating ordered categorical variables using panel data: a generalized ordered probit model with an autofit procedure," Journal of Economics and Econometrics, Economics and Econometrics Society, vol. 54(1), pages 7-23.
    • Handle: RePEc:eei:journl:v:54:y:2011:i:1:p:7-23
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    6. 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.
    7. 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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    13. Massimiliano Agovino & Massimiliano Cerciello & Aniello Ferraro & Antonio Garofalo, 2021. "Spatial analysis of wildfire incidence in the USA: the role of climatic spillovers," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(4), pages 6084-6105, April.
    14. Gulay Avsar & Roger Ham & W. Kathy Tannous, 2017. "Factors Influencing the Incidence of Obesity in Australia: A Generalized Ordered Probit Model," IJERPH, MDPI, vol. 14(2), pages 1-13, February.
    15. Brown, Heather & van der Pol, Marjon, 2015. "Intergenerational transfer of time and risk preferences," Journal of Economic Psychology, Elsevier, vol. 49(C), pages 187-204.
    16. Tarja Viitanen, 2014. "Parental divorce and other determinants of interpersonal trust: Evidence from HILDA panel data," Australian Journal of Labour Economics (AJLE), Bankwest Curtin Economics Centre (BCEC), Curtin Business School, vol. 17(1), pages 35-53.
    17. Chaudhuri, Kausik & Reilly, Kevin T. & Spencer, David A., 2015. "Job satisfaction, age and tenure: A generalized dynamic random effects model," Economics Letters, Elsevier, vol. 130(C), pages 13-16.
    18. Jürgen Bierbaumer-Polly & Werner Hölzl, 2016. "Business Cycle Dynamics and Firm Heterogeneity. Evidence for Austria Using Survey Data," WIFO Working Papers 504, WIFO.
    19. Işıl Şirin Selçuk & Altuğ Murat Köktaş & Şükrü Anıl Toygar, 2023. "Socioeconomic factors affecting the probability of obesity: evidence from a nationwide survey in Turkey," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(1), pages 239-255, February.
    20. Zhang, Xiao-Bing & Hassen, Sied, 2017. "Household fuel choice in urban China: evidence from panel data," Environment and Development Economics, Cambridge University Press, vol. 22(4), pages 392-413, August.
    21. Yang, Qingqing & Rosenman, Robert, 2015. "Adjusting Self-Assessed Health for Potential Bias Using a Random-Effects Generalized Ordered Probit model," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 205217, Agricultural and Applied Economics Association.
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    23. Laetitia Duval & François-­charles Wolff, 2015. "" I Even Met Happy Gypsies " : Life Satisfaction of Roma Youth in the Balkans," Working Papers hal-01219250, HAL.

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    More about this item

    Keywords

    Generalized ordered probit; panel data; autofit; self-assessed health.;
    All these keywords.

    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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