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Are unit root tests useful for univariate time series forecasts with different orders of integration? A Monte Carlo study

Author

Listed:
  • Adam J. Check

    (U.S. Bank)

  • Ming Chien Lo

    (Metropolitan State University)

  • Kwok Ping Tsang

    (Virginia Tech)

Abstract

In this paper, we consider univariate forecasts made when using stationary, near unit root, and unit root data. Like Diebold and Kilian (2000), we conduct a Monte Carlo experiment investigating the usefulness of unit root tests prior to forming univariate forecasts. In our experiment, we consider more than one unit root test and also vary the order of integration in the time series. We find that unit root tests are indeed useful for forecasting, especially when the series has a large number of in-sample observations. However, the choice of unit test matters. Using root mean square error as a criterion for forecast performance, we find that the Philips-Perron test has an edge over the augmented Dickey-Fuller test and the Kwiatkowski–Phillips–Schmidt–Shin test. We recommend practitioners to be mindful of the choice of test, as the KPSS test is the default used in the forecast package in R, following Hyndman and Khandakar (2008), but the Philips-Perron test is available as an option in that package.

Suggested Citation

  • Adam J. Check & Ming Chien Lo & Kwok Ping Tsang, 2023. "Are unit root tests useful for univariate time series forecasts with different orders of integration? A Monte Carlo study," Economics Bulletin, AccessEcon, vol. 43(1), pages 203-244.
    • Handle: RePEc:ebl:ecbull:eb-22-00135
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    References listed on IDEAS

    as
    1. Efstathios Paparoditis & Dimitris N. Politis, 2018. "The asymptotic size and power of the augmented Dickey–Fuller test for a unit root," Econometric Reviews, Taylor & Francis Journals, vol. 37(9), pages 955-973, October.
    2. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    3. Diebold, Francis X & Kilian, Lutz, 2000. "Unit-Root Tests Are Useful for Selecting Forecasting Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 18(3), pages 265-273, July.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Augmented Dickey-Fuller; KPSS; Philips-Perron; Forecasting Algorithm; Monte Carlo; Unit Root Test;
    All these keywords.

    JEL classification:

    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
    • E2 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment

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