Estimation bias and bias correction in reduced rank autoregressions

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Estimation bias and bias correction in reduced rank autoregressions. / Nielsen, Heino Bohn.

I: Econometric Reviews, Bind 38, Nr. 3, 16.03.2019, s. 332-349.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

Nielsen, HB 2019, 'Estimation bias and bias correction in reduced rank autoregressions', Econometric Reviews, bind 38, nr. 3, s. 332-349. https://doi.org/10.1080/07474938.2017.1308065

APA

Nielsen, H. B. (2019). Estimation bias and bias correction in reduced rank autoregressions. Econometric Reviews, 38(3), 332-349. https://doi.org/10.1080/07474938.2017.1308065

Vancouver

Nielsen HB. Estimation bias and bias correction in reduced rank autoregressions. Econometric Reviews. 2019 mar. 16;38(3): 332-349. https://doi.org/10.1080/07474938.2017.1308065

Author

Nielsen, Heino Bohn. / Estimation bias and bias correction in reduced rank autoregressions. I: Econometric Reviews. 2019 ; Bind 38, Nr. 3. s. 332-349.

Bibtex

@article{56616c5283804dbabffd3e43d3c3d95c,
title = "Estimation bias and bias correction in reduced rank autoregressions",
abstract = "This paper characterizes the finite-sample bias of the maximum likelihood estimator (MLE) in a reduced rank vector autoregression and suggests two simulation-based bias corrections. One is a simple bootstrap implementation that approximates the bias at the MLE. The other is an iterative root-finding algorithm implemented using stochastic approximation methods. Both algorithms are shown to be improvements over the MLE, measured in terms of mean square error and mean absolute deviation. An illustration to US macroeconomic time series is given.",
keywords = "Bias correction, bootstrap, cointegration, estimation bias, stochastic approximation, vector autoregression, C32, C13",
author = "Nielsen, {Heino Bohn}",
year = "2019",
month = mar,
day = "16",
doi = "10.1080/07474938.2017.1308065",
language = "English",
volume = "38",
pages = " 332--349",
journal = "Econometric Reviews",
issn = "0747-4938",
publisher = "Taylor & Francis",
number = "3",

}

RIS

TY - JOUR

T1 - Estimation bias and bias correction in reduced rank autoregressions

AU - Nielsen, Heino Bohn

PY - 2019/3/16

Y1 - 2019/3/16

N2 - This paper characterizes the finite-sample bias of the maximum likelihood estimator (MLE) in a reduced rank vector autoregression and suggests two simulation-based bias corrections. One is a simple bootstrap implementation that approximates the bias at the MLE. The other is an iterative root-finding algorithm implemented using stochastic approximation methods. Both algorithms are shown to be improvements over the MLE, measured in terms of mean square error and mean absolute deviation. An illustration to US macroeconomic time series is given.

AB - This paper characterizes the finite-sample bias of the maximum likelihood estimator (MLE) in a reduced rank vector autoregression and suggests two simulation-based bias corrections. One is a simple bootstrap implementation that approximates the bias at the MLE. The other is an iterative root-finding algorithm implemented using stochastic approximation methods. Both algorithms are shown to be improvements over the MLE, measured in terms of mean square error and mean absolute deviation. An illustration to US macroeconomic time series is given.

KW - Bias correction

KW - bootstrap

KW - cointegration

KW - estimation bias

KW - stochastic approximation

KW - vector autoregression

KW - C32

KW - C13

U2 - 10.1080/07474938.2017.1308065

DO - 10.1080/07474938.2017.1308065

M3 - Journal article

AN - SCOPUS:85019263841

VL - 38

SP - 332

EP - 349

JO - Econometric Reviews

JF - Econometric Reviews

SN - 0747-4938

IS - 3

ER -

ID: 186156542