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Econometrics 2014, 2(1), 45-71; doi:10.3390/econometrics2010045
Article

Bias-Correction in Vector Autoregressive Models: A Simulation Study

 and
*
CREATES, Department of Economics and Business, Aarhus University, Fuglesangs Alle 4, DK-8210 Aarhus V, Denmark
* Author to whom correspondence should be addressed.
Received: 23 October 2013 / Revised: 10 January 2014 / Accepted: 17 February 2014 / Published: 13 March 2014
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Abstract

We analyze the properties of various methods for bias-correcting parameter estimates in both stationary and non-stationary vector autoregressive models. First, we show that two analytical bias formulas from the existing literature are in fact identical. Next, based on a detailed simulation study, we show that when the model is stationary this simple bias formula compares very favorably to bootstrap bias-correction, both in terms of bias and mean squared error. In non-stationary models, the analytical bias formula performs noticeably worse than bootstrapping. Both methods yield a notable improvement over ordinary least squares. We pay special attention to the risk of pushing an otherwise stationary model into the non-stationary region of the parameter space when correcting for bias. Finally, we consider a recently proposed reduced-bias weighted least squares estimator, and we find that it compares very favorably in non-stationary models.
Keywords: bias reduction; VAR model; analytical bias formula; bootstrap; iteration; Yule-Walker; non-stationary system; skewed and fat-tailed data bias reduction; VAR model; analytical bias formula; bootstrap; iteration; Yule-Walker; non-stationary system; skewed and fat-tailed data
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).
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Engsted, T.; Pedersen, T.Q. Bias-Correction in Vector Autoregressive Models: A Simulation Study. Econometrics 2014, 2, 45-71.

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