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Ordered response models and non-random personality traits: Monte Carlo simulations and a practical guide

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  • Geishecker, Ingo
  • Riedl, Maximilian

Abstract

The paper compares different estimation strategies of ordered response models in the presence of non-random unobserved heterogeneity. By running Monte Carlo simulations with a range of randomly generated panel data of differing cross-sectional and longitudinal dimension sizes, we assess the consistency and efficiency of standard models such as linear fixed effects, ordered and conditional logit, and several different binary recoding procedures. Among the binary recoding procedures analyzed are the conditional ordered logit estimator proposed by Ferrer-i-Carbonell and Frijters (2004) that recently has gained some popularity in the analysis of individual well-being, as well as the new developed "Blow-Up and Cluster" (BUC) estimator of Baetschmann et al. (2011). The Ferrer-i-Carbonell and Frijters estimator (FCF) performs best if the number of observations is large and the number of categories on the ordered scale is small. However, the BUC method performs similarly well and even outperforms the FCF estimator if the number of categories on the ordered scale is large. If the researcher is only interested in the relative size of coefficients with respect to a baseline, however, the easy-to-compute linear fixed effects model delivers essentially the same results as the more elaborate binary recoding schemes.

Suggested Citation

  • Geishecker, Ingo & Riedl, Maximilian, 2012. "Ordered response models and non-random personality traits: Monte Carlo simulations and a practical guide," University of Göttingen Working Papers in Economics 116, University of Goettingen, Department of Economics.
  • Handle: RePEc:zbw:cegedp:116
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    8. Marcus Klemm, 2011. "You Don‘t Know what You‘ve got till It‘s Gone! Unemployment and Intertemporal Changes in Self-Reported Life Satisfaction," Ruhr Economic Papers 0297, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen.
    9. Robin Zoutenbier, 2016. "The impact of matching mission preferences on well-being at work," Economics of Governance, Springer, vol. 17(3), pages 295-315, August.
    10. Kseniya Abanokova & Hai-Anh H. Dang & Michael M. Lokshin, 2020. "The Important Role of Equivalence Scales: Household Size, Composition, and Poverty Dynamics in the Russian Federation," Working Papers 539, ECINEQ, Society for the Study of Economic Inequality.
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    13. Geishecker, Ingo & Riedl, Maximilian & Frijters, Paul, 2012. "Offshoring and job loss fears: An econometric analysis of individual perceptions," Labour Economics, Elsevier, vol. 19(5), pages 738-747.
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    23. Christopher L Ambrey & Christopher M Fleming & Matthew Manning, 2013. "The life satisfaction approach to estimating the cost of crime: An individual's willingness-to-pay for crime reduction," Discussion Papers in Economics economics:201301, Griffith University, Department of Accounting, Finance and Economics.
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    More about this item

    Keywords

    fixed effects ordered logit; ordered responses; happiness;
    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
    • I31 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - General Welfare, Well-Being

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