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

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

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

  • Pfarr, Christian & Schmid, Andreas & Schneider, Udo, 2010. "Estimating ordered categorical variables using panel data: a generalized ordered probit model with an autofit procedure," MPRA Paper 23203, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:23203
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    4. Greene,William H. & Hensher,David A., 2010. "Modeling Ordered Choices," Cambridge Books, Cambridge University Press, number 9780521194204, September.
    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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    Cited by:

    1. Maurice Mutisya & Moses W. Ngware & Caroline W. Kabiru & Ngianga-bakwin Kandala, 2016. "The effect of education on household food security in two informal urban settlements in Kenya: a longitudinal analysis," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 8(4), pages 743-756, August.
    2. Zara Daghbashyan & Björn Hårsman, 2014. "University choice and entrepreneurship," Small Business Economics, Springer, vol. 42(4), pages 729-746, April.
    3. Valerii Baidin & Christopher J. Gerry & Maria Kaneva, 2021. "How Self-Rated is Self-Rated Health? Exploring the Role of Individual and Institutional Factors in Reporting Heterogeneity in Russia," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 155(2), pages 675-696, June.
    4. Eiji Yamamura, 2014. "Smokers’ Sexual Behavior and Their Satisfaction with Family Life," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 118(3), pages 1229-1247, September.
    5. 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.
    6. 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.
    7. 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.
    8. Laetitia Duval & François-Charles Wolff, 2016. "“I even met happy gypsies”," The Economics of Transition, The European Bank for Reconstruction and Development, vol. 24(4), pages 727-764, October.
    9. Pfarr, Christian & Schmid, Andreas & Schneider, Udo, 2011. "Reporting Heterogeneity in Self-Assessed Health among Elderly Europeans: The Impact of Mental and Physical Health Status," MPRA Paper 29900, University Library of Munich, Germany.
    10. 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.
    11. Samanthi Durage & Lina Kattan & S. Wirasinghe & Janaka Ruwanpura, 2014. "Evacuation behaviour of households and drivers during a tornado," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 71(3), pages 1495-1517, April.
    12. Jürgen Bierbaumer & Werner Hölzl, 2015. "Business Cycle Dynamics and Firm Heterogeneity. Evidence for Austria Using Survey Data," WIFO Working Papers 504, WIFO.
    13. 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.
    14. Naiwei Chen & Ho-Chyuan Chen, 2021. "Religion, Marriage and Happiness – Evidence from Taiwan," Applied Research in Quality of Life, Springer;International Society for Quality-of-Life Studies, vol. 16(1), pages 259-299, 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. 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.
    18. Hassen, Sied, 2018. "The effect of farmyard manure on the continued and discontinued use of inorganic fertilizer in Ethiopia: An ordered probit analysis," Land Use Policy, Elsevier, vol. 72(C), pages 523-532.
    19. 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.
    20. 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.
    21. 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.
    22. 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.
    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:

    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software
    • 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
    • I10 - Health, Education, and Welfare - - Health - - - General

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