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Power of Tests in Binary Response Models

Author

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  • N.E. Savin

    (Univ. of Iowa)

  • Allan Wurtz

    (Univ of Iowa)

Abstract

Most hypotheses in binary response models are composite. The null hypothesis is usually that one or more slope coefficients are zero. Typically, the sequence of alternatives of interest is one in which the slope coefficients are increasing in absolute value. In this paper, we prove that the power goes to zero for this sequence of alternatives of interest in cases which often occur in practice. The practical implication is that for the sequence of alternatives of interest the power is nonmonotonic. This is true for any non-randomized test with size less than one and for a wide class of binary response models which includes the logit and probit models.

Suggested Citation

  • N.E. Savin & Allan Wurtz, 1996. "Power of Tests in Binary Response Models," Econometrics 9606001, University Library of Munich, Germany, revised 05 Jul 1996.
    • Handle: RePEc:wpa:wuwpem:9606001
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    References listed on IDEAS

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    1. Nelson, Forrest D & Savin, N E, 1990. "The Danger of Extrapolating Asymptotic Local Power," Econometrica, Econometric Society, vol. 58(4), pages 977-981, July.
    2. Killingsworth, Mark R. & Heckman, James J., 1987. "Female labor supply: A survey," Handbook of Labor Economics, in: O. Ashenfelter & R. Layard (ed.), Handbook of Labor Economics, edition 1, volume 1, chapter 2, pages 103-204, Elsevier.
    3. Amemiya, Takeshi, 1981. "Qualitative Response Models: A Survey," Journal of Economic Literature, American Economic Association, vol. 19(4), pages 1483-1536, December.
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    Cited by:

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    2. Frischknecht, Bart D. & Eckert, Christine & Geweke, John & Louviere, Jordan J., 2014. "A simple method for estimating preference parameters for individuals," International Journal of Research in Marketing, Elsevier, vol. 31(1), pages 35-48.
    3. Jouneau-Sion, Frederic & Torres, Olivier, 2006. "MMC techniques for limited dependent variables models: Implementation by the branch-and-bound algorithm," Journal of Econometrics, Elsevier, vol. 133(2), pages 479-512, August.

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    More about this item

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs

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