Department of Economics and Business Economics

Bias-correction in vector autoregressive models: A simulation study

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Bias-correction in vector autoregressive models : A simulation study. / Engsted, Tom; Pedersen, Thomas Quistgaard.

In: Econometrics, Vol. 2, No. 1, 2014, p. 45-71.

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@article{69c8661344214f899df3170e049a45db,
title = "Bias-correction in vector autoregressive models: A simulation study",
abstract = "We analyze the properties of various methods for bias-correcting parameterestimates 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 noticeablyworse 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 thatit compares very favorably in non-stationary models.",
keywords = "Bias reduction, VAR model, Analytical bias formula, Bootstrap, Iteration, Yule-Walker, Non-stationary system, Skewed and fat-tailed data",
author = "Tom Engsted and Pedersen, {Thomas Quistgaard}",
year = "2014",
doi = "10.3390/econometrics2010045",
language = "English",
volume = "2",
pages = "45--71",
journal = "Econometrics",
issn = "2225-1146",
publisher = "MDPI AG",
number = "1",

}

RIS

TY - JOUR

T1 - Bias-correction in vector autoregressive models

T2 - A simulation study

AU - Engsted, Tom

AU - Pedersen, Thomas Quistgaard

PY - 2014

Y1 - 2014

N2 - We analyze the properties of various methods for bias-correcting parameterestimates 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 noticeablyworse 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 thatit compares very favorably in non-stationary models.

AB - We analyze the properties of various methods for bias-correcting parameterestimates 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 noticeablyworse 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 thatit compares very favorably in non-stationary models.

KW - Bias reduction

KW - VAR model

KW - Analytical bias formula

KW - Bootstrap

KW - Iteration

KW - Yule-Walker

KW - Non-stationary system

KW - Skewed and fat-tailed data

U2 - 10.3390/econometrics2010045

DO - 10.3390/econometrics2010045

M3 - Journal article

VL - 2

SP - 45

EP - 71

JO - Econometrics

JF - Econometrics

SN - 2225-1146

IS - 1

ER -