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Related Experiment Videos

A double bootstrap method to analyze linear models with autoregressive error terms.

S D McKnight1, J W McKean, B E Huitema

  • 1Department of Clinical Outcomes and Resource Management, Toledo Hospital, Ohio, USA.

Psychological Methods
|August 11, 2000
PubMed
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A novel bootstrap method enhances linear model analysis for time-series data with autoregressive errors. This approach improves small-sample inference for regression and autoregressive parameters.

Area of Science:

  • Statistics
  • Econometrics
  • Behavioral Sciences

Background:

  • Linear models with autoregressive errors are common in time-series analysis.
  • Small-sample inference in these models presents significant challenges.
  • Existing methods may lack accuracy or applicability in specific scenarios.

Purpose of the Study:

  • To propose a new, robust method for analyzing linear models with autoregressive errors.
  • To address the challenges of small-sample inference for both regression and autoregressive parameters.
  • To provide a versatile tool applicable across various scientific disciplines.

Main Methods:

  • A novel methodology employing a double bootstrap procedure.
  • The first bootstrap application refines autoregressive parameter estimates by adjusting for bias.

Related Experiment Videos

  • The second bootstrap application provides reliable estimates of standard errors for all model parameters.

    • Main Results:

    • Theoretical and Monte Carlo simulations confirm the method's asymptotic and small-sample properties.
    • Demonstrated superior performance compared to established time-series methodologies.
    • Provided practical examples showcasing the advantages of the proposed approach.

    Conclusions:

    • The new double bootstrap method offers a significant advancement in analyzing linear models with autoregressive errors.
    • It provides accurate and reliable inference, particularly in small-sample situations.
    • The method is broadly applicable and advantageous over existing techniques for time-series intervention models and other linear model problems.