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A score test for non-nested hypotheses with applications to discrete data models

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  • J. M. C. Santos Silva

    (ISEG|Universidade Técnica de Lisboa, R. do Quelhas 6, 1200 Lisboa, Portugal)

Abstract

In this paper it is shown that a convenient score test against non-nested alternatives can be constructed from the linear combination of the likelihood functions of the competing models. This is essentially a test for the correct specification of the conditional distribution of the variable of interest. Given its characteristics, the proposed test is particularly attractive to check the distributional assumptions in models for discrete data. The usefulness of the test is illustrated with an application to models for recreational boating trips. Copyright © 2001 John Wiley & Sons, Ltd.

Suggested Citation

  • J. M. C. Santos Silva, 2001. "A score test for non-nested hypotheses with applications to discrete data models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 16(5), pages 577-597.
    • Handle: RePEc:jae:japmet:v:16:y:2001:i:5:p:577-597
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    5. Silva João M. C. Santos & Tenreyro Silvana & Windmeijer Frank, 2015. "Testing Competing Models for Non-negative Data with Many Zeros," Journal of Econometric Methods, De Gruyter, vol. 4(1), pages 1-18, January.
    6. 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.
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    8. Santos Silva, J.M.C. & Tenreyro, Silvana & Wei, Kehai, 2014. "Estimating the extensive margin of trade," Journal of International Economics, Elsevier, vol. 93(1), pages 67-75.
    9. Otsu, Taisuke & Seo, Myung Hwan & Whang, Yoon-Jae, 2012. "Testing for non-nested conditional moment restrictions using unconditional empirical likelihood," Journal of Econometrics, Elsevier, vol. 167(2), pages 370-382.
    10. Eyal, Yonatan & Beenstock, Michael, 2008. "Sign reversal in LIVE treatment effect estimates: The effect of vocational training on unemployment duration," Labour Economics, Elsevier, vol. 15(5), pages 1102-1125, October.
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    12. Juan Mora & Ana I. Moro, 2006. "Consistent Specification Test For Ordered Discrete Choice Models," Working Papers. Serie AD 2006-17, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
    13. 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.
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    15. Yufei Jin & Roderick Rejesus & Bertis Little, 2005. "Binary choice models for rare events data: a crop insurance fraud application," Applied Economics, Taylor & Francis Journals, vol. 37(7), pages 841-848.
    16. Nakatani, Tomoaki & Sato, Kazuo, 2005. "Truncation and Endogenous Stratification in Various Count Data Models for Recreation Demand Analysis," SSE/EFI Working Paper Series in Economics and Finance 615, Stockholm School of Economics.
    17. Isabel Mendes & Isabel Proença, 2009. "Measuring the Social Recreation Per-Day Net Benefit of Wildlife Amenities of a National Park: A Count-Data Travel Cost Approach," Working Papers Department of Economics 2009/35, ISEG - Lisbon School of Economics and Management, Department of Economics, Universidade de Lisboa.
    18. William Greene, 2007. "Discrete Choice Modeling," Working Papers 07-6, New York University, Leonard N. Stern School of Business, Department of Economics.
    19. M. T. Aparicio & I. Villan�a, 2012. "Selection criteria for overlapping binary Models," Documentos de Trabajo dt2012-01, Facultad de Ciencias Económicas y Empresariales, Universidad de Zaragoza.
    20. Kuan, Chung-Ming & Lin, Hsin-Yi, 2010. "An encompassing test for non-nested quantile regression models," Economics Letters, Elsevier, vol. 107(2), pages 257-260, May.
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    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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