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Estimation bias and bias correction in reduced rank autoregressions

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  • Heino Bohn Nielsen

Abstract

This paper characterizes the finite-sample bias of the maximum likelihood estimator (MLE) in a reduced rank vector autoregression and suggests two simulation-based bias corrections. One is a simple bootstrap implementation that approximates the bias at the MLE. The other is an iterative root-finding algorithm implemented using stochastic approximation methods. Both algorithms are shown to be improvements over the MLE, measured in terms of mean square error and mean absolute deviation. An illustration to US macroeconomic time series is given.

Suggested Citation

  • Heino Bohn Nielsen, 2019. "Estimation bias and bias correction in reduced rank autoregressions," Econometric Reviews, Taylor & Francis Journals, vol. 38(3), pages 332-349, March.
    • Handle: RePEc:taf:emetrv:v:38:y:2019:i:3:p:332-349
    DOI: 10.1080/07474938.2017.1308065
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    Cited by:

    1. Kurita, Takamitsu, 2020. "Normalising cointegrating relationships subject to long-run exclusion," Economics Letters, Elsevier, vol. 192(C).

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