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Consistency of kernel variance estimators for sums of semiparametric linear processes

Author

Listed:
  • James Davidson

    (Cardiff Business School, UK)

  • Robert M. De Jong

    (Department of Economics, Michigan State University, USA)

Abstract

Conditions are derived for the consistency of kernel estimators of the variance of a sum of dependent heterogeneous random variables, with a representation as moving averages of near-epoch dependent functions of a mixing process. Fourth moments are not generally required. The conditions permit more dependence than a purely non-parametric representation allows, and may be close to those of the best-known conditions for the functional central limit theorem. The class of permitted kernel functions is different from those usually considered, but can approximate most of the usual choices arbitrarily closely, and can be extended to include them subject to a seemingly innocuous extra condition on the random process. Copyright Royal Economic Society 2002

Suggested Citation

  • James Davidson & Robert M. De Jong, 2002. "Consistency of kernel variance estimators for sums of semiparametric linear processes," Econometrics Journal, Royal Economic Society, vol. 5(1), pages 160-175, June.
    • Handle: RePEc:ect:emjrnl:v:5:y:2002:i:1:p:160-175
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    Cited by:

    1. Davidson, James, 2020. "A new consistency proof for HAC variance estimators," Economics Letters, Elsevier, vol. 186(C).
    2. Yasutomo Murasawa, 2009. "Do coincident indicators have one-factor structure?," Empirical Economics, Springer, vol. 36(2), pages 339-365, May.
    3. Hirukawa, Masayuki, 2023. "Robust Covariance Matrix Estimation in Time Series: A Review," Econometrics and Statistics, Elsevier, vol. 27(C), pages 36-61.
    4. Margherita Gerolimetto & Isabella Procidano, 2008. "A test for fractional cointegration using the sieve bootstrap," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 17(3), pages 373-391, July.

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