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