Convergence of spectral density estimators in the locally stationary framework

Rafael Kawka

Econometrics and Statistics, 2022, vol. 24, issue C, 94-115

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.

Keywords: Locally stationary process; Spectral density; Kernel estimator; HAC-inference; Global approximation; Test for stationarity (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecosta:v:24:y:2022:i:c:p:94-115

DOI: 10.1016/j.ecosta.2020.06.001

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