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Parameter Estimation of Standard AR(1) and MA(1) Models Driven by a Non-I.I.D. Noise

In: Research Papers in Statistical Inference for Time Series and Related Models

Author

Listed:
  • Violetta Dalla

    (National and Kapodistrian University of Athens)

  • Liudas Giraitis

    (Queen Mary University of London)

  • Murad S. Taqqu

    (Boston University)

Abstract

The use of a non-i.i.d. noise in parametric modeling of stationary time series can lead to unexpected distortions of the standard errors and confidence intervals in parameter estimation. We consider AR(1) and MA(1) models and motivate the need for correction of standard errors when these are generated by a non-i.i.d. noise. The impact of the noise on the standard errors and confidence intervals is illustrated with Monte Carlo simulations using various types of noise.

Suggested Citation

  • Violetta Dalla & Liudas Giraitis & Murad S. Taqqu, 2023. "Parameter Estimation of Standard AR(1) and MA(1) Models Driven by a Non-I.I.D. Noise," Springer Books, in: Yan Liu & Junichi Hirukawa & Yoshihide Kakizawa (ed.), Research Papers in Statistical Inference for Time Series and Related Models, chapter 0, pages 155-172, Springer.
    • Handle: RePEc:spr:sprchp:978-981-99-0803-5_6
    DOI: 10.1007/978-981-99-0803-5_6
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