Department of Economics and Business Economics

Bias-correction in vector autoregressive models: A simulation study

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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.
Original languageEnglish
Pages (from-to)45-71
Number of pages27
Publication statusPublished - 2014

    Research areas

  • Bias reduction, VAR model, Analytical bias formula, Bootstrap, Iteration, Yule-Walker, Non-stationary system, Skewed and fat-tailed data

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