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Alternative Versions of the RESET Test for Binary Response Index Models: A Comparative Study

Author

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  • Esmeralda A. Ramalho
  • Joaquim J. S. Ramalho

Abstract

Binary response index models may be affected by several forms of misspecification, which range from pure functional form problems (e.g. incorrect specification of the link function, neglected heterogeneity, heteroskedasticity) to various types of sampling issues (e.g. covariate measurement error, response misclassification, endogenous stratification, missing data). In this paper we examine the ability of several versions of the RESET test to detect such misspecifications in an extensive Monte Carlo simulation study. We find that: (i) the best variants of the RESET test are clearly those based on one or two fitted powers of the response index; and (ii) the loss of power resulting from using the RESET instead of a test directed against a specific type of misspecification is very small in many cases.
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Suggested Citation

  • Esmeralda A. Ramalho & Joaquim J. S. Ramalho, 2012. "Alternative Versions of the RESET Test for Binary Response Index Models: A Comparative Study," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 74(1), pages 107-130, February.
    • Handle: RePEc:bla:obuest:v:74:y:2012:i:1:p:107-130
    DOI: j.1468-0084.2011.00654.x
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    Cited by:

    1. J. M. C. Santos Silva & Silvana Tenreyro, 2015. "Trading Partners and Trading Volumes: Implementing the Helpman–Melitz–Rubinstein Model Empirically," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 77(1), pages 93-105, February.
    2. Stephanie Thomas, 2016. "Playing by the rules? Agreement between predicted and observed binary choices," Department of Economics Working Papers 2016-12, McMaster University.
    3. Esmeralda A. Ramalho & Joaquim J. S. Ramalho & José M. R. Murteira, 2014. "A Generalized Goodness-of-functional Form Test for Binary and Fractional Regression Models," Manchester School, University of Manchester, vol. 82(4), pages 488-507, July.
    4. Francesco Fallucchi & Andrea Mercatanti & Jan Niederreiter, 2021. "Identifying types in contest experiments," International Journal of Game Theory, Springer;Game Theory Society, vol. 50(1), pages 39-61, March.
    5. Ramalho, Esmeralda A. & Ramalho, Joaquim J.S., 2010. "Is neglected heterogeneity really an issue in binary and fractional regression models? A simulation exercise for logit, probit and loglog models," Computational Statistics & Data Analysis, Elsevier, vol. 54(4), pages 987-1001, April.
    6. Tomás del Barrio Casto & William Nilsson & Andrés J. Picazo-Tadeo, 2013. "How wrong can you be, without noticing? Further evidence on speci?cation errors in the Conditional Logit," Working Papers 1318, Department of Applied Economics II, Universidad de Valencia.
    7. Esmeralda A. Ramalho & Joaquim J. S. Ramalho, 2017. "Moment-based estimation of nonlinear regression models with boundary outcomes and endogeneity, with applications to nonnegative and fractional responses," Econometric Reviews, Taylor & Francis Journals, vol. 36(4), pages 397-420, April.
    8. Kumar, Sunil Mitra, 2013. "Does Access to Formal Agricultural Credit Depend on Caste?," World Development, Elsevier, vol. 43(C), pages 315-328.
    9. Esmeralda Ramalho & Joaquim Ramalho & Jose M.R. Murteira, 2012. "A supremum-type RESET test for binary choice models," Economics Bulletin, AccessEcon, vol. 32(1), pages 905-912.

    More about this item

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities

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