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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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    5. 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.
    6. 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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    7. 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.
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    13. Nicolas Lampach & Kene Boun My & Sandrine Spaeter, 2016. "Risk, Ambiguity and Efficient Liability Rules: An experiment," Working Papers of BETA 2016-30, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
    14. Heather Brown, 2016. "Birds of a Feather Flock Together: Correlation in BMI Categories in Long-Term Relationships," Australian Economic Review, The University of Melbourne, Melbourne Institute of Applied Economic and Social Research, vol. 49(4), pages 453-470, December.
    15. Laetitia Duval & François-­charles Wolff, 2015. "" I Even Met Happy Gypsies " : Life Satisfaction of Roma Youth in the Balkans," Working Papers hal-01219250, HAL.
    16. Kaneva, Maria & Baidin, Valerii, 2018. "Heterogeneity in reporting self-assessed health of the Russians," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 51, pages 102-125.
    17. Jürgen Bierbaumer & Werner Hölzl, 2015. "Business Cycle Dynamics and Firm Heterogeneity. Evidence for Austria Using Survey Data," WIFO Working Papers 504, WIFO.
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    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.
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    23. Udo Schneider & Christian Pfarr & Brit Schneider & Volker Ulrich, 2012. "I feel good! Gender differences and reporting heterogeneity in self-assessed health," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 13(3), pages 251-265, June.

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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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