Bias-correction in vector autoregressive models: A simulation study

Tom Engsted and Thomas Pedersen ()

CREATES Research Papers from Department of Economics and Business Economics, Aarhus University

Abstract: We analyze and compare the properties of various methods for bias-correcting parameter estimates in vector autoregressions. 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 this simple and easy-to-use analytical bias formula compares very favorably to the more standard but also more computer intensive bootstrap bias-correction method, both in terms of bias and mean squared error. Both methods yield a notable improvement over both OLS and a recently proposed WLS estimator. We also investigate the properties of an iterative scheme when applying the analytical bias formula, and we ?find that this can imply slightly better fi?nite-sample properties for very small sample sizes while for larger sample sizes there is no gain by iterating. Finally, we also pay special attention to the risk of pushing an otherwise stationary model into the non-stationary region of the parameter space during the process of correcting for bias.

Keywords: Bias reduction; VAR model; analytical bias formula; bootstrap; iteration; Yule-Walker; non-stationary system; skewed and fat-tailed data. (search for similar items in EconPapers)
JEL-codes: C13 C32 (search for similar items in EconPapers)
Pages: 33
Date: 2011-05-13
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
References: View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Related works:
Journal Article: Bias-Correction in Vector Autoregressive Models: A Simulation Study (2014)
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Persistent link: https://EconPapers.repec.org/RePEc:aah:create:2011-18

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