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Robust nonnested hypothesis testing

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  • Sunil Sapra

Abstract

This article presents robust J and encompassing tests for testing nonnested hypotheses in the presence of outliers in the data. The proposed tests are based on least absolute deviations (LAD) and M-estimators unlike the conventional J and encompassing tests, which are based on least squares or maximum likelihood estimators. These tests can lead to more reliable inference in the presence of outliers than tests based on nonrobust estimators. The tests are illustrated with applications to two economic data sets and an artificially generated data set and compared with their nonrobust counterparts.

Suggested Citation

  • Sunil Sapra, 2007. "Robust nonnested hypothesis testing," Applied Economics Letters, Taylor & Francis Journals, vol. 15(1), pages 1-4.
    • Handle: RePEc:taf:apeclt:v:15:y:2007:i:1:p:1-4
    DOI: 10.1080/13504850600605994
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    1. Davidson, Russell & MacKinnon, James G, 1981. "Several Tests for Model Specification in the Presence of Alternative Hypotheses," Econometrica, Econometric Society, vol. 49(3), pages 781-793, May.
    2. Mizon, Grayham E & Richard, Jean-Francois, 1986. "The Encompassing Principle and Its Application to Testing Non-nested Hypotheses," Econometrica, Econometric Society, vol. 54(3), pages 657-678, May.
    3. Peracchi, Franco, 1991. "Robust M-Tests," Econometric Theory, Cambridge University Press, vol. 7(1), pages 69-84, March.
    4. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
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    Cited by:

    1. Francisco Cribari-Neto & Sadraque E.F. Lucena, 2015. "Nonnested hypothesis testing in the class of varying dispersion beta regressions," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(5), pages 967-985, May.

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