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

Bias-Correction in Vector Autoregressive Models: A Simulation Study

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Received: 23 October 2013; in revised form: 10 January 2014 / Accepted: 17 February 2014 / Published: 13 March 2014
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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
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MDPI and ACS Style

Engsted, T.; Pedersen, T.Q. Bias-Correction in Vector Autoregressive Models: A Simulation Study. Econometrics 2014, 2, 45-71.

AMA Style

Engsted T, Pedersen TQ. Bias-Correction in Vector Autoregressive Models: A Simulation Study. Econometrics. 2014; 2(1):45-71.

Chicago/Turabian Style

Engsted, Tom; Pedersen, Thomas Q. 2014. "Bias-Correction in Vector Autoregressive Models: A Simulation Study." Econometrics 2, no. 1: 45-71.

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