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

Adam Check (), Ming Chien Lo () and Kwok Ping Tsang ()
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Adam Check: U.S. Bank
Ming Chien Lo: Metropolitan State University
Kwok Ping Tsang: Virginia Tech

Economics Bulletin, 2023, vol. 43, issue 1, 203 - 244

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.

Keywords: Augmented Dickey-Fuller; KPSS; Philips-Perron; Forecasting Algorithm; Monte Carlo; Unit Root Test (search for similar items in EconPapers)
JEL-codes: C8 E2 (search for similar items in EconPapers)
Date: 2023-03-30
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