Bias-Correction in Vector Autoregressive Models: A Simulation Study

Econometrics

Tom Engsted¹ 

[1] CREATES, Department of Economics and Business, Aarhus University, Fuglesangs Alle 4, DK-8210 Aarhus V, Denmark; E-Mail

关键词: bias reduction;    VAR model;    analytical bias formula;    bootstrap;    iteration;    Yule-Walker;    non-stationary system;    skewed and fat-tailed data;   

DOI  :  10.3390/econometrics2010045

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland.