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Estimating MA Parameters through Factorization of the Autocovariance Matrix and an MA†Sieve Bootstrap

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  • Timothy L. McMurry
  • Dimitris N. Politis

Abstract

A new method to estimate the moving†average (MA) coefficients of a stationary time series is proposed. The new approach is based on the modified Cholesky factorization of a consistent estimator of the autocovariance matrix. Convergence rates are established, and the new estimates are used to implement an MA†type sieve bootstrap. Finite†sample simulations corroborate the good performance of the proposed methodology.

Suggested Citation

  • Timothy L. McMurry & Dimitris N. Politis, 2018. "Estimating MA Parameters through Factorization of the Autocovariance Matrix and an MA†Sieve Bootstrap," Journal of Time Series Analysis, Wiley Blackwell, vol. 39(3), pages 433-446, May.
    • Handle: RePEc:bla:jtsera:v:39:y:2018:i:3:p:433-446
    DOI: 10.1111/jtsa.12296
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    Cited by:

    1. Paulo M. D. C. Parente & Richard J. Smith, 2021. "Quasi‐maximum likelihood and the kernel block bootstrap for nonlinear dynamic models," Journal of Time Series Analysis, Wiley Blackwell, vol. 42(4), pages 377-405, July.
    2. Jonas Krampe & Timothy L. McMurry, 2021. "Estimating wold matrices and vector moving average processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 42(2), pages 201-221, March.
    3. Ma, Bin & Guo, Xing & Li, Penghui, 2023. "Adaptive energy management strategy based on a model predictive control with real-time tuning weight for hybrid energy storage system," Energy, Elsevier, vol. 283(C).

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