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Structural change tests for simulated method of moments

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  • Ghysels, Eric
  • Guay, Alain

Abstract

Simulation-based estimation methods have become more widely used in recent years. We propose a set of tests for structural change in models estimates via Simulated Method of Moments (see Duffie and Singleton (1993)). These tests extend the recent work of Andrews (1993) and Sowell (1996a, b) which covered Generalized Method of Moments estimators not involving simulation. We derive the asymptotic distributions of various tests. We show that the number of simulations does not affect the asymptotic distribution nor the asymptotic local power of tests for structural change. A Monte Carlo investigation of the finite sample size and power reveals, however, that simulation uncertainty does affect the properties of tests. Nevertheless, even a relatively small number of simulations suffices to obtain tests with desirable small sample size and power properties. Les méthodes simulées d'estimation sont de plus en plus utilisées pour l'estimation et l'évaluation des modèles struturels. Dans cette étude, nous introduisons un ensemble de tests de stabilité pour les modèles estimés à l'aide de la méthode des moments simulés (voir Duffie et Singleton (1993)). Ces tests sont basés sur les travaux récents, dans le cadre de la méthode des moments généralisés, de Andrews (1993) et Sowell (1996a, b). Nous obtenons la loi asymptotique de ces tests et nous montrons que cette loi ainsi que la puissance locale asymptotique ne dépendent pas du nombre de simulations. Une étude de Monte-Carlo révèle qu'en petit échantillon le nombre de simulations influence le niveau et la puissance des tests. Cependant, un nombre restreint de simulations semble suffisant pour obtenir des bonnes propriétés de petit échantillon.
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  • Ghysels, Eric & Guay, Alain, 2003. "Structural change tests for simulated method of moments," Journal of Econometrics, Elsevier, vol. 115(1), pages 91-123, July.
    • Handle: RePEc:eee:econom:v:115:y:2003:i:1:p:91-123
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    Cited by:

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    3. Alain Guay & Olivier Scaillet, 1999. "Indirect Inference, Nuisance Parameter and Threshold Moving Average," Cahiers de recherche CREFE / CREFE Working Papers 95, CREFE, Université du Québec à Montréal.
    4. Anindya Biswas & Biswajit Mandal, 2016. "Estimating Preference Parameters From Stock Returns Using Simulated Method Of Moments," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 11(01), pages 1-13, March.
    5. Prosper Donovon & Alastair R. Hall, 2015. "GMM and Indirect Inference: An appraisal of their connections and new results on their properties under second order identification," Economics Discussion Paper Series 1505, Economics, The University of Manchester.
    6. Prosper Dovonon & Alastair R. Hall, 2017. "The Asymptotic Properties of GMM and Indirect Inference Under Second-Order Identification," Economics Discussion Paper Series 1705, Economics, The University of Manchester.
    7. Dovonon, Prosper & Hall, Alastair R., 2018. "The asymptotic properties of GMM and indirect inference under second-order identification," Journal of Econometrics, Elsevier, vol. 205(1), pages 76-111.
    8. Sen, Amit & Hall, Alastair, 1999. "Two further aspects of some new tests for structural stability," Structural Change and Economic Dynamics, Elsevier, vol. 10(3-4), pages 431-443, December.

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    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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