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Nonminimum phase non-Gaussian autoregressive processes.

F J Breidt1, R A Davis, K S Lii

  • 1Department of Statistics, Colorado State University, Fort Collins, CO 80523, USA.

Proceedings of the National Academy of Sciences of the United States of America
|January 1, 1990
PubMed
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This study details non-Gaussian autoregressive models and efficient coefficient estimation methods. Focus is placed on nonminimum phase models, requiring specific density function conditions for accuracy.

Area of Science:

  • Statistics
  • Time Series Analysis
  • Econometrics

Background:

  • Autoregressive (AR) models are fundamental in time series analysis.
  • Non-Gaussian distributions present challenges for standard AR model estimation.
  • Nonminimum phase models require specialized estimation techniques.

Purpose of the Study:

  • To describe the structure of non-Gaussian autoregressive schemes.
  • To develop asymptotically efficient estimation methods for AR model coefficients.
  • To investigate nonminimum phase models within this framework.

Main Methods:

  • Development of estimation algorithms for non-Gaussian AR models.
  • Analysis of conditions for asymptotic efficiency, including density function properties.

Related Experiment Videos

  • Focus on estimation techniques applicable to nonminimum phase systems.

    • Main Results:

    • Established methods for estimating coefficients in non-Gaussian AR models.
    • Identified conditions (smoothness, positivity of density function) for efficient estimation.
    • Demonstrated applicability to nonminimum phase models.

    Conclusions:

    • The proposed methods offer asymptotically efficient estimation for non-Gaussian AR models.
    • The findings are particularly relevant for nonminimum phase models.
    • This work advances the analysis of complex time series data.