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Bias-Correction in Vector Autoregressive Models: A Simulation Study

  • Tom Engsted

    ()

    (CREATES, Department of Economics and Business, Aarhus University, Fuglesangs Alle 4, DK-8210 Aarhus V, Denmark)

  • Thomas Q. Pedersen

    ()

    (CREATES, Department of Economics and Business, Aarhus University, Fuglesangs Alle 4, DK-8210 Aarhus V, Denmark)

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.

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Article provided by MDPI, Open Access Journal in its journal Econometrics.

Volume (Year): 2 (2014)
Issue (Month): 1 (March)
Pages: 45-71

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Handle: RePEc:gam:jecnmx:v:2:y:2014:i:1:p:45-71:d:34027
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