Prewhitened long-run variance estimation robust to nonstationarity

Alessandro Casini and Pierre Perron

Journal of Econometrics, 2024, vol. 242, issue 1

Abstract: We introduce a nonparametric nonlinear VAR prewhitened long-run variance (LRV) estimator for the construction of standard errors robust to autocorrelation and heteroskedasticity that can be used for hypothesis testing in a variety of contexts including the linear regression model. Existing methods either are theoretically valid only under stationarity and have poor finite-sample properties under nonstationarity (i.e., fixed-b methods), or are theoretically valid under the null hypothesis but lead to tests that are not consistent under nonstationary alternative hypothesis (i.e., both fixed-b and traditional HAC estimators). The proposed estimator accounts explicitly for nonstationarity, unlike previous prewhitened procedures which are known to be unreliable, and leads to tests with accurate null rejection rates and good monotonic power. We also establish MSE bounds for LRV estimation that are sharper than previously established and use them to determine the data-dependent bandwidths.

Keywords: Asymptotic minimax MSE; Data-dependent bandwidths; HAC; HAR; Long-run variance; Nonstationarity; Prewhitening; Spectral density (search for similar items in EconPapers)
JEL-codes: C12 C13 C18 C22 C32 C51 (search for similar items in EconPapers)
Date: 2024
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Working Paper: Prewhitened Long-Run Variance Estimation Robust to Nonstationarity (2024)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:242:y:2024:i:1:s0304407624001404

DOI: 10.1016/j.jeconom.2024.105794

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