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Blockwise empirical entropy tests for time series regressions

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  • Francesco Bravo

Abstract

This paper shows how the empirical entropy (also known as exponential likelihood or non-parametric tilting) method can be used to test general parametric hypothesis in time series regressions. To capture the weak dependence of the observations, the paper uses blocking techniques which are also used in the bootstrap literature on time series. Monte Carlo evidence suggests that the proposed test statistics have better finite-sample properties than conventional test statistics such as the Wald statistic. Copyright 2005 Blackwell Publishing Ltd.

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  • Francesco Bravo, 2005. "Blockwise empirical entropy tests for time series regressions," Journal of Time Series Analysis, Wiley Blackwell, vol. 26(2), pages 185-210, March.
  • Handle: RePEc:bla:jtsera:v:26:y:2005:i:2:p:185-210
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    References listed on IDEAS

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    1. Bauer, Dietmar & Wagner, Martin, 2002. "Estimating cointegrated systems using subspace algorithms," Journal of Econometrics, Elsevier, vol. 111(1), pages 47-84, November.
    2. Kapetanios, George, 2004. "A note on modelling core inflation for the UK using a new dynamic factor estimation method and a large disaggregated price index dataset," Economics Letters, Elsevier, vol. 85(1), pages 63-69, October.
    3. Kascha, Christian & Mertens, Karel, 2009. "Business cycle analysis and VARMA models," Journal of Economic Dynamics and Control, Elsevier, vol. 33(2), pages 267-282, February.
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    5. Alfredo García-Hiernaux & José Casals & Miguel Jerez, 2012. "Estimating the system order by subspace methods," Computational Statistics, Springer, vol. 27(3), pages 411-425, September.
    6. Dietmar Bauer, 2005. "Comparing the CCA Subspace Method to Pseudo Maximum Likelihood Methods in the case of No Exogenous Inputs," Journal of Time Series Analysis, Wiley Blackwell, vol. 26(5), pages 631-668, September.
    7. Alfredo García Hiernaux & José Casals Carro & Miguel Jerez, 2005. "Fast estimation methods for time series models in state-space form," Documentos de Trabajo del ICAE 0504, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    8. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    9. Bauer, Dietmar, 2005. "Estimating Linear Dynamical Systems Using Subspace Methods," Econometric Theory, Cambridge University Press, vol. 21(01), pages 181-211, February.
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    Cited by:

    1. Yuichi Kitamura, 2006. "Empirical Likelihood Methods in Econometrics: Theory and Practice," Levine's Bibliography 321307000000000307, UCLA Department of Economics.
    2. Allen, Jason & Gregory, Allan W. & Shimotsu, Katsumi, 2011. "Empirical likelihood block bootstrapping," Journal of Econometrics, Elsevier, vol. 161(2), pages 110-121, April.
    3. Daniel Nordman, 2008. "An empirical likelihood method for spatial regression," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 68(3), pages 351-363, November.
    4. Daniel J. Nordman & Helle Bunzel & Soumendra N. Lahiri, 2012. "A Non-standard Empirical Likelihood for Time Series," CREATES Research Papers 2012-55, Department of Economics and Business Economics, Aarhus University.
    5. Francesco Bravo, 2016. "Local Information Theoretic Methods for smooth Coefficients Dynamic Panel Data Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(5), pages 690-708, September.
    6. Yuichi Kitamura, 2006. "Empirical Likelihood Methods in Econometrics: Theory and Practice," Cowles Foundation Discussion Papers 1569, Cowles Foundation for Research in Economics, Yale University.
    7. Marc G. Genton & Peter Hall, 2016. "A tilting approach to ranking influence," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(1), pages 77-97, January.

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