Testing for trend

Fabio Busetti and Andrew Harvey ()
Additional contact information
Andrew Harvey: Cambridge University

No 614, Temi di discussione (Economic working papers) from Bank of Italy, Economic Research and International Relations Area

Abstract: The paper examines various tests for assessing whether a time series model requires a slope component. We first consider the simple t-test on the mean of first differences and show that it achieves high power against the alternative hypothesis of a stochastic nonstationary slope as well as against a purely deterministic slope. The test may be modified, parametrically or nonparametrically to deal with serial correlation. Using both local limiting power arguments and finite sample Monte Carlo results, we compare the t-test with the nonparametric tests of Vogelsang (1998) and with a modified stationarity test. Overall the t-test seems a good choice, particularly if it is implemented by fitting a parametric model to the data. When standardized by the square root of the sample size, the simple t-statistic, with no correction for serial correlation, has a limiting distribution if the slope is stochastic. We investigate whether it is a viable test for the null hypothesis of a stochastic slope and conclude that its value may be limited by an inability to reject a small deterministic slope. Empirical illustrations are provided using series of relative prices in the euro-area and data on global temperature.

Keywords: Cram�r-von Mises distribution; stationarity test; stochastic trend; unit root; unobserved component. (search for similar items in EconPapers)
JEL-codes: C22 C52 (search for similar items in EconPapers)
Date: 2007-02
New Economics Papers: this item is included in nep-cba, nep-ecm and nep-ets
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (19)

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Related works:
Journal Article: TESTING FOR TREND (2008)
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Persistent link: https://EconPapers.repec.org/RePEc:bdi:wptemi:td_614_07

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