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GMM Based Tests for Locally Misspecified Models

Author

Listed:
  • Walter Sosa Escudero

    () (Department of Economics, Universidad de San Andres)

  • Anil K. Bera

    (University of Illinois)

Abstract

This paper shows that the standard Newey-West GMM based test is sensitive to the presence of locally misspecified alternatives. In particular, such test is shown to have incorrect size when the null model is locally contaminated, making the test spuriously reject the null hypothesis even when it is correct. After obtaining the distribution of the Newey-West test statistic under the misspecified alternative, the paper derives a Bera-Yoon modified test based on a restricted GMM estimator, which has the correct size under local misspecification. The paper can be seen as extending the likelihood based results of Saikkonen (1989), Davidson and MacKinnon (1987) and Bera and Yoon (1993) to the GMM framework, which does not require full specification of the underlying probabilistic model.

Suggested Citation

  • Walter Sosa Escudero & Anil K. Bera, 2006. "GMM Based Tests for Locally Misspecified Models," Working Papers 93, Universidad de San Andres, Departamento de Economia, revised Sep 2006.
  • Handle: RePEc:sad:wpaper:93
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    File URL: ftp://webacademicos.udesa.edu.ar/pub/econ/doc93.pdf
    File Function: First version, 2006
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    Cited by:

    1. Roberto Cortes Conde, 2008. "Spanish America Colonial Patterns: The Rio de La Plata," Working Papers 96, Universidad de San Andres, Departamento de Economia, revised Mar 2008.

    More about this item

    Keywords

    Generalized method of moments; testing; misspecification;

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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