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HAR Testing for Spurious Regression in Trend

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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.

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  • Peter C.B. Phillips & Yonghui Zhang & Xiaohu Wang, 2018. "HAR Testing for Spurious Regression in Trend," Cowles Foundation Discussion Papers 2153, Cowles Foundation for Research in Economics, Yale University.
  • Handle: RePEc:cwl:cwldpp:2153
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    Keywords

    HAR inference; Karhunen-Loève representation; Spurious regression; t-statistics;

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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