HAR Testing for Spurious Regression in Trend

Peter Phillips, Yonghui Zhang and Xiaohu Wang
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Yonghui Zhang: Renmin University of China
Xiaohu Wang: The Chinese University of Hong Kong

No 2153, Cowles Foundation Discussion Papers from Cowles Foundation for Research in Economics, Yale University

Abstract: The usual t test, the t test based on heteroskedasticity and autocorrelation consistent (HAC) covariance matrix estimators, and the heteroskedasticity and autocorrelation robust (HAR) test are three statistics that are widely used in applied econometric work. The use of these significance tests in trend regression is of particular interest given the potential for spurious relationships in trend formulations. Following a longstanding tradition in the spurious regression literature, this paper investigates the asymptotic and finite sample properties of these test statistics in several spurious regression contexts, including regression of stochastic trends on time polynomials and regressions among independent random walks. Concordant with existing theory (Phillips, 1986, 1998; Sun, 2004, 2014), the usual t test and HAC standardized test fail to control size as the sample size n \to \infty in these spurious formulations, whereas HAR tests converge to well-defined limit distributions in each case and therefore have the capacity to be consistent and control size. However, it is shown that when the number of trend regressors K \to \infty, all three statistics, including the HAR test, diverge and fail to control size as n \to \infty. These findings are relevant to high dimensional nonstationary time series regressions.

Keywords: HAR inference; Karhunen-Lo've representation; Spurious regression; t-statistics (search for similar items in EconPapers)
JEL-codes: C12 C14 C23 (search for similar items in EconPapers)
Pages: 39 pages
Date: 2018-12
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
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Citations: View citations in EconPapers (1)

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Journal Article: HAR Testing for Spurious Regression in Trend (2019)
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