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A Data-Driven Nonparametric Specification Test For Dynamic Regression Models

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  • Guay, Alain
  • Guerre, Emmanuel
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    Abstract

    The paper introduces a new nonparametric specification test for dynamic regression models. The test combines chi-square statistics based on Fourier series regression. A data-driven choice of the regression order, which uses the square root of the number of Fourier coefficients, is proposed. The benefits of the new test are (1) the selection procedure produces explicit and chi-square critical values that give a finite-sample size close to the nominal size; (2) the test is adaptive rate-optimal and detects local alternatives converging to the null with a rate that can be made arbitrarily close to the parametric rate. Simulation experiments illustrate the practical relevance of the new test.The first author acknowledges financial support from the Fonds Qu b cois de la Recherche sur la Soci t et la Culture (FQRSC). The second author acknowledges financial support from LSTA.

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    Bibliographic Info

    Article provided by Cambridge University Press in its journal Econometric Theory.

    Volume (Year): 22 (2006)
    Issue (Month): 04 (August)
    Pages: 543-586

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    Handle: RePEc:cup:etheor:v:22:y:2006:i:04:p:543-586_06

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    Cited by:
    1. George Kapetanios & Andrew P. Blake, 2007. "Boosting Estimation of RBF Neural Networks for Dependent Data," Working Papers 588, Queen Mary, University of London, School of Economics and Finance.
    2. Escanciano, Juan Carlos & Mayoral, Silvia, 2010. "Data-driven smooth tests for the martingale difference hypothesis," Computational Statistics & Data Analysis, Elsevier, vol. 54(8), pages 1983-1998, August.
    3. Andrea Vaona, 2008. "The sensitivity of nonparametric misspecification tests to disturbance autocorrelation," Quaderni della facoltà di Scienze economiche dell'Università di Lugano 0803, USI Università della Svizzera italiana.
    4. Gbaguidi, David Sedo, 2011. "Expectations Impact on the Effectiveness of the Inflation-Real Activity Trade-Off," MPRA Paper 35482, University Library of Munich, Germany.
    5. George Kapetanios & Andrew P. Blake, 2007. "Testing the Martingale Difference Hypothesis Using Neural Network Approximations," Working Papers 601, Queen Mary, University of London, School of Economics and Finance.
    6. Guay, Alain & Guerre, Emmanuel & Lazarová, Štěpána, 2013. "Robust adaptive rate-optimal testing for the white noise hypothesis," Journal of Econometrics, Elsevier, vol. 176(2), pages 134-145.

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