Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/103629
Authors: 
Engsted, Tom
Pedersen, Thomas Q.
Year of Publication: 
2014
Citation: 
[Journal:] Econometrics [ISSN:] 2225-1146 [Publisher:] MDPI [Place:] Basel [Volume:] 2 [Year:] 2014 [Issue:] 1 [Pages:] 45-71
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.
Subjects: 
bias reduction
VAR model
analytical bias formula
bootstrap
iteration
Yule-Walker
non-stationary system
skewed and fat-tailed data
JEL: 
C13
C32
Persistent Identifier of the first edition: 
Creative Commons License: 
http://creativecommons.org/licenses/by/3.0/
Document Type: 
Article
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