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Specification tests for non-Gaussian maximum likelihood estimators

Author

Listed:
  • Gabriele Fiorentini

    (Università di Firenze, Italy; Rimini Centre for Economic Analysis)

  • Enrique Sentana

    (CEMFI, Spain)

Abstract
We propose generalised DWH specification tests which simultaneously compare three or more likelihood-based estimators of conditional mean and variance parameters in multivariate conditionally heteroskedastic dynamic regression models. Our tests are useful for Garch models and in many empirically relevant macro and finance applications involving Vars and multivariate regressions. To design powerful and reliable tests, we determine the rank deficiencies of the differences between the estimators' asymptotic covariance matrices under the null of correct specification, and take into account that some parameters remain consistently estimated under the alternative of distributional misspecification. Finally, we provide finite sample results through Monte Carlo simulations.

Suggested Citation

  • Gabriele Fiorentini & Enrique Sentana, 2018. "Specification tests for non-Gaussian maximum likelihood estimators," Working Paper series 18-22, Rimini Centre for Economic Analysis.
    • Handle: RePEc:rim:rimwps:18-22
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    Cited by:

    1. Fiorentini, Gabriele & Sentana, Enrique, 2019. "Consistent non-Gaussian pseudo maximum likelihood estimators," Journal of Econometrics, Elsevier, vol. 213(2), pages 321-358.
    2. Dante Amengual & Gabriele Fiorentini & Enrique Sentana, 2022. "Moment tests of independent components," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 13(1), pages 429-474, May.
    3. Fiorentini, Gabriele & Sentana, Enrique, 2023. "Discrete mixtures of normals pseudo maximum likelihood estimators of structural vector autoregressions," Journal of Econometrics, Elsevier, vol. 235(2), pages 643-665.

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    More about this item

    Keywords

    Durbin-Wu-Hausman Tests; Partial Adaptivity; Semiparametric Estimators; Singular Covariance Matrices;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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