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Convergence of spectral density estimators in the locally stationary framework

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  • Kawka, Rafael

Abstract

Asymptotic properties of classical kernel estimators for the spectral density are studied in the locally stationary framework. In particular, it is shown that for a locally stationary process standard spectral density estimators consistently estimate the time-averaged spectral density. This result is complemented by some illustrative examples and applications including HAC-inference in the multiple linear regression model, a simple visual tool for the detection of unconditional heteroskedasticity and a test for covariance stationarity.

Suggested Citation

  • Kawka, Rafael, 2022. "Convergence of spectral density estimators in the locally stationary framework," Econometrics and Statistics, Elsevier, vol. 24(C), pages 94-115.
    • Handle: RePEc:eee:ecosta:v:24:y:2022:i:c:p:94-115
    DOI: 10.1016/j.ecosta.2020.06.001
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    1. Casini, Alessandro & Perron, Pierre, 2024. "Prewhitened long-run variance estimation robust to nonstationarity," Journal of Econometrics, Elsevier, vol. 242(1).

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