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Robust Covariance Matrix Estimation in Time Series: A Review

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  • Hirukawa, Masayuki

Abstract

In the analysis of economic, financial and other time series, long-run variance estimators play an important role in estimating model parameters more efficiently and drawing more accurate statistical inference on the parameters. A non-technical review of long-run variance estimation is provided. Both parametric and nonparametric estimators are discussed. Kernel methods are dominant among all estimation procedures, and therefore recent developments in kernel-smoothed estimators and related inference are presented. The information given can help practitioners decide on a suitable long-run variance estimator.

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  • Hirukawa, Masayuki, 2023. "Robust Covariance Matrix Estimation in Time Series: A Review," Econometrics and Statistics, Elsevier, vol. 27(C), pages 36-61.
    • Handle: RePEc:eee:ecosta:v:27:y:2023:i:c:p:36-61
    DOI: 10.1016/j.ecosta.2021.12.001
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    More about this item

    Keywords

    Bandwidth; Generalized method of moments; Heteroskedasticity and autocorrelation robust inference; Kernel; Long-run variance; Positive semi-definite;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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