Du er her: » Tom Engsted » Publikationer » Bias-correction in vector autoregressive models

Tom Engsted

Bias-correction in vector autoregressive models: A simulation study

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avis › Tidsskriftartikel › Forskning › peer review

Standard

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

I: Econometrics, Bind 2, Nr. 1, 2014, s. 45-71.

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avis › Tidsskriftartikel › Forskning › peer review

Harvard

Engsted, T & Pedersen, TQ 2014, 'Bias-correction in vector autoregressive models: A simulation study', Econometrics, bind 2, nr. 1, s. 45-71. https://doi.org/10.3390/econometrics2010045

APA

Engsted, T., & Pedersen, T. Q. (2014). Bias-correction in vector autoregressive models: A simulation study. Econometrics, 2(1), 45-71. https://doi.org/10.3390/econometrics2010045

CBE

Engsted T, Pedersen TQ. 2014. Bias-correction in vector autoregressive models: A simulation study. Econometrics. 2(1):45-71. https://doi.org/10.3390/econometrics2010045

MLA

Engsted, Tom og Thomas Quistgaard Pedersen. "Bias-correction in vector autoregressive models: A simulation study". Econometrics. 2014, 2(1). 45-71. https://doi.org/10.3390/econometrics2010045

Vancouver

Engsted T, Pedersen TQ. Bias-correction in vector autoregressive models: A simulation study. Econometrics. 2014;2(1):45-71. https://doi.org/10.3390/econometrics2010045

Author

Engsted, Tom ; Pedersen, Thomas Quistgaard. / Bias-correction in vector autoregressive models : A simulation study. I: Econometrics. 2014 ; Bind 2, Nr. 1. s. 45-71.

Bibtex

@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 -