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Related Experiment Videos

A simple and powerful test for autocorrelated errors in OLS intervention models.

B E Huitema1, J W McKean

  • 1Department of Psychology, Western Michigan University, Kalamazoo 49008-5052, USA.

Psychological Reports
|October 12, 2000
PubMed
Summary

A new statistical test for interrupted time-series regression models addresses weaknesses in existing methods. This approach offers accurate error independence evaluation without inconclusive results, improving time-series analysis.

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Area of Science:

  • Statistics
  • Biostatistics
  • Econometrics

Background:

  • Interrupted time-series (ITS) regression models are crucial for evaluating interventions.
  • A key assumption in ITS models is the independence of errors.
  • Existing tests like Mood's runs test and Durbin-Watson (D-W) bounds test have limitations, including poor small sample performance and inconclusive results.

Purpose of the Study:

  • To introduce a novel, simple-to-compute statistical test for evaluating the independence of errors in ITS models.
  • To address the shortcomings of existing tests, specifically the lack of an inconclusive region and the provision of exact p-values.

Main Methods:

  • A new statistical test for error independence in ITS models was developed.
  • The test's properties were evaluated using Monte Carlo simulations.

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  • Comparisons were made against established tests, including Mood's runs test, D-W bounds test, and D-W beta test.

    • Main Results:

    • The proposed test demonstrates good Type I error and power properties compared to existing methods.
    • The test is simple to compute, requiring no specialized software.
    • It provides an exact p-value and avoids the 'inconclusive' results often seen with the D-W bounds test.

    Conclusions:

    • The novel statistical test is a reliable and practical alternative for routine evaluation of error independence in ITS models.
    • Its desirable properties, particularly the absence of an inconclusive region and exact p-value, enhance its utility in time-series analysis.