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Erscheinungsjahr: 
2014
Quellenangabe: 
[Journal:] Econometrics [ISSN:] 2225-1146 [Volume:] 2 [Issue:] 1 [Publisher:] MDPI [Place:] Basel [Year:] 2014 [Pages:] 45-71
Verlag: 
MDPI, Basel
Zusammenfassung: 
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.
Schlagwörter: 
bias reduction
VAR model
analytical bias formula
bootstrap
iteration
Yule-Walker
non-stationary system
skewed and fat-tailed data
JEL: 
C13
C32
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