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Computing and estimating information matrices of weak arma models

Author

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  • Boubacar Mainassara, Yacouba
  • Carbon, Michel
  • Francq, Christian

Abstract

Numerous time series admit "weak" autoregressive-moving average (ARMA) representations, in which the errors are uncorrelated but not necessarily independent nor martingale differences. The statistical inference of this general class of models requires the estimation of generalized Fisher information matrices. We give analytic expressions and propose consistent estimators of these matrices, at any point of the parameter space. Our results are illustrated by means of Monte Carlo experiments and by analyzing the dynamics of daily returns and squared daily returns of financial series.

Suggested Citation

  • Boubacar Mainassara, Yacouba & Carbon, Michel & Francq, Christian, 2010. "Computing and estimating information matrices of weak arma models," MPRA Paper 27685, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:27685
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    References listed on IDEAS

    as
    1. Francq, Christian & Zakoïan, Jean-Michel, 2009. "Testing the Nullity of GARCH Coefficients: Correction of the Standard Tests and Relative Efficiency Comparisons," Journal of the American Statistical Association, American Statistical Association, vol. 104(485), pages 313-324.
    2. Mak, T. K. & Wong, H. & Li, W. K., 1997. "Estimation of nonlinear time series with conditional heteroscedastic variances by iteratively weighted least squares," Computational Statistics & Data Analysis, Elsevier, vol. 24(2), pages 169-178, April.
    3. André Klein & Guy Mélard, 2004. "An algorithm for computing the asymptotic fisher information matrix for seasonal SISO models," Journal of Time Series Analysis, Wiley Blackwell, vol. 25(5), pages 627-648, September.
    4. Christian Francq & Jean-Michel Zakoïan, 2009. "Bartlett's formula for a general class of nonlinear processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 30(4), pages 449-465, July.
    5. Francq, Christian & Zako an, Jean-Michel, 2000. "Estimating Weak Garch Representations," Econometric Theory, Cambridge University Press, vol. 16(05), pages 692-728, October.
    6. Andrews, Donald W K, 1991. "Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation," Econometrica, Econometric Society, vol. 59(3), pages 817-858, May.
    7. Christian Francq & Jean-Michel Zakoïan, 1997. "Covariance Matrix Estimation for Estimators of Mixing Wold's Arma," Working Papers 97-19, Center for Research in Economics and Statistics.
    8. Francq, Christian & Roy, Roch & Zakoian, Jean-Michel, 2005. "Diagnostic Checking in ARMA Models With Uncorrelated Errors," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 532-544, June.
    9. E. J. Godolphin & S. R. Bane, 2006. "On the Evaluation of the Information Matrix for Multiplicative Seasonal Time-Series Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 27(2), pages 167-190, March.
    10. Francq, Christian & Zakoïan, Jean-Michel, 2007. "HAC estimation and strong linearity testing in weak ARMA models," Journal of Multivariate Analysis, Elsevier, vol. 98(1), pages 114-144, January.
    11. Robert F. Engle & Jeffrey R. Russell, 1998. "Autoregressive Conditional Duration: A New Model for Irregularly Spaced Transaction Data," Econometrica, Econometric Society, vol. 66(5), pages 1127-1162, September.
    12. Sejling, Ken & Madsen, Henrik & Holst, Jan & Holst, Ulla & Englund, Jan-Eric, 1994. "Methods for recursive robust estimation of AR parameters," Computational Statistics & Data Analysis, Elsevier, vol. 17(5), pages 509-536, June.
    13. Francq, Christian & ZakoI¨an, Jean-Michel, 2008. "Deriving the autocovariances of powers of Markov-switching GARCH models, with applications to statistical inference," Computational Statistics & Data Analysis, Elsevier, vol. 52(6), pages 3027-3046, February.
    14. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 33(1), pages 125-132.
    15. Christian Francq & Jean-Michel Zakoïan, 2006. "Linear-representation Based Estimation of Stochastic Volatility Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 33(4), pages 785-806.
    16. André Klein & Guy Melard, 1990. "Fisher's information matrix for seasonal autoregressive-moving average models," ULB Institutional Repository 2013/13718, ULB -- Universite Libre de Bruxelles.
    17. Engle, Robert F., 1984. "Wald, likelihood ratio, and Lagrange multiplier tests in econometrics," Handbook of Econometrics,in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 2, chapter 13, pages 775-826 Elsevier.
    18. André Klein & Guy Melard, 2004. "An algorithm for computing the asymptotic Fisher information matrix for seasonal SISO models," ULB Institutional Repository 2013/13746, ULB -- Universite Libre de Bruxelles.
    19. Kuhn, E. & Lavielle, M., 2005. "Maximum likelihood estimation in nonlinear mixed effects models," Computational Statistics & Data Analysis, Elsevier, vol. 49(4), pages 1020-1038, June.
    20. Das, Sonjoy & Spall, James C. & Ghanem, Roger, 2010. "Efficient Monte Carlo computation of Fisher information matrix using prior information," Computational Statistics & Data Analysis, Elsevier, vol. 54(2), pages 272-289, February.
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    Cited by:

    1. Bao, Yong & Hua, Ying, 2014. "On the Fisher information matrix of a vector ARMA process," Economics Letters, Elsevier, vol. 123(1), pages 14-16.
    2. Yacouba Boubacar Maïnassara & Célestin C. Kokonendji, 2016. "Modified Schwarz and Hannan–Quinn information criteria for weak VARMA models," Statistical Inference for Stochastic Processes, Springer, vol. 19(2), pages 199-217, July.
    3. Min, Aleksey & Czado, Claudia, 2014. "SCOMDY models based on pair-copula constructions with application to exchange rates," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 523-535.
    4. Christian Francq & Jean-Michel Zakoïan, 2013. "Estimating the Marginal Law of a Time Series With Applications to Heavy-Tailed Distributions," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(4), pages 412-425, October.

    More about this item

    Keywords

    Asymptotic relative efficiency (ARE); Bahadur's slope; Information matrices; Lagrange Multiplier test; Nonlinear processes; Wald test; Weak ARMA models;

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: 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
    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics

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