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A Score Test for Non-nested Hypotheses with Applications to Discrete Data Models

Author

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  • J M C Santos Silva

    (ISEG, Universidade Técnica de Lisboa)

Abstract

This paper suggests that a convenient score test against non- nested alternatives can be constructed from the linear combination of the likelihood functions of the competing models. It is shown that this procedure is essentially a test for the correct specification of the conditional distribution of the variable of interest. As in Models for discrete data it is often necessary to fully specify the conditional distribution of the variate of interest, the test proposed here is particularly attractive in this context. The usefulness of the proposed tests is illustrated with applications to discrete choice and count data models.

Suggested Citation

  • J M C Santos Silva, 1996. "A Score Test for Non-nested Hypotheses with Applications to Discrete Data Models," Discussion Papers 96-28 ISSN 1350-6722, University College London, Department of Economics.
    • Handle: RePEc:wuk:ucloec:9628
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    Cited by:

    1. James E. Anderson & Yoto V. Yotov, 2020. "Pound for Pound Export Diversification," CESifo Working Paper Series 8800, CESifo.
    2. Muhammad Asali & Aamer S. Abu‐Qarn & Michael Beenstock, 2017. "The cycle of violence in the Second Intifada: Causality in nonlinear vector autoregressive models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(6), pages 1197-1205, September.
    3. 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 29-46, January.
    4. 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.
    5. 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.
    6. Wilde, Joachim, 2008. "A simple representation of the Bera-Jarque-Lee test for probit models," Economics Letters, Elsevier, vol. 101(2), pages 119-121, November.
    7. 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).
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. William Greene, 2007. "Discrete Choice Modeling," Working Papers 07-6, New York University, Leonard N. Stern School of Business, Department of Economics.
    13. Teresa Aparicio & Inmaculada Villanúa, 2022. "Selection Criteria for Overlapping Binary Models—A Simulation Study," Mathematics, MDPI, vol. 10(3), pages 1-15, February.
    14. Franses Philip Hans & Paap Richard, 2013. "Common large innovations across nonlinear time series," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 17(3), pages 251-263, May.
    15. Joao Santos Silva Santos Silva & Silvana Tenreyro & Frank Windmeijer, 2010. "Is it different for zeros? Discriminating between models for non-negative data with many zeros," CeMMAP working papers CWP20/10, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    16. 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.
    17. Isabel Proenca & Isabel Menes, 2000. "Measuring the Average Per Day Net Benefit of Non-consumptive Wildlife - Associated Recreation For a National Park: a Count-Data Travel Cost Approach," Regional and Urban Modeling 283600078, EcoMod.
    18. 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.
    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.
    21. Durham, Catherine A., 2007. "The Impact of Environmental and Health Motivations on the Organic Share of Produce Purchases," Agricultural and Resource Economics Review, Cambridge University Press, vol. 36(2), pages 304-320, October.

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    JEL classification:

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

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