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An Improved Selection Test between Autoregressive and Moving Average Disturbances in Regression Models

Author

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  • Nguimkeu Pierre

    (Department of Economics, Georgia State University, PO Box 3992, Atlanta, GA 30302-3992, USA)

Abstract

This paper proposes an improved likelihood-based method to test the hypothesis that the disturbances of a linear regression model are generated by a first-order autoregressive process against the alternative that they follow a first-order moving average scheme. Compared with existing tests which usually rely on the asymptotic properties of the estimators, the proposed method has remarkable accuracy, particularly in small samples. Simulations studies are provided to show the superior accuracy of the method compared to the traditional tests. An empirical example using Canada real interest rate illustrates the implementation of the proposed method in practice.

Suggested Citation

  • Nguimkeu Pierre, 2016. "An Improved Selection Test between Autoregressive and Moving Average Disturbances in Regression Models," Journal of Time Series Econometrics, De Gruyter, vol. 8(1), pages 41-54, January.
    • Handle: RePEc:bpj:jtsmet:v:8:y:2016:i:1:p:41-54:n:4
    DOI: 10.1515/jtse-2014-0036
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    More about this item

    Keywords

    autoregressive errors; moving average errors; likelihood analysis; p-value;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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