Estimation bias and bias correction in reduced rank autoregressions

Heino Bohn Nielsen

doi:10.1080/07474938.2017.1308065

Estimation bias and bias correction in reduced rank autoregressions

Heino Bohn Nielsen^*

^*Corresponding author af dette arbejde

1 Citationer (Scopus)

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.

Originalsprog	Engelsk
Tidsskrift	Econometric Reviews
Vol/bind	38
Udgave nummer	3
Sider (fra-til)	332-349
Antal sider	18
ISSN	0747-4938
DOI	https://doi.org/10.1080/07474938.2017.1308065
Status	Udgivet - 16 mar. 2019

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10.1080/07474938.2017.1308065

Citationsformater

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AB - 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.

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KW - bootstrap

KW - cointegration

KW - estimation bias

KW - stochastic approximation

KW - vector autoregression

KW - C32

KW - C13

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Estimation bias and bias correction in reduced rank autoregressions

Abstract

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