Heavy tails of OLS

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Standard

Heavy tails of OLS. / Mikosch, Thomas Valentin; de Vries, Casper.

I: Journal of Econometrics, Bind 172, Nr. 2, 2013, s. 205-221.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

Mikosch, TV & de Vries, C 2013, 'Heavy tails of OLS', Journal of Econometrics, bind 172, nr. 2, s. 205-221. https://doi.org/10.1016/j.jeconom.2012.08.015

APA

Mikosch, T. V., & de Vries, C. (2013). Heavy tails of OLS. Journal of Econometrics, 172(2), 205-221. https://doi.org/10.1016/j.jeconom.2012.08.015

Vancouver

Mikosch TV, de Vries C. Heavy tails of OLS. Journal of Econometrics. 2013;172(2):205-221. https://doi.org/10.1016/j.jeconom.2012.08.015

Author

Mikosch, Thomas Valentin ; de Vries, Casper. / Heavy tails of OLS. I: Journal of Econometrics. 2013 ; Bind 172, Nr. 2. s. 205-221.

Bibtex

@article{54f8dc8321e446239c5b4f4bd4795d84,
title = "Heavy tails of OLS",
abstract = "Suppose the tails of the noise distribution in a regression exhibit power law behavior. Then thedistribution of the OLS regression estimator inherits this tail behavior. This is relevant for regressions involving financial data. We derive explicit finite sample expressions for the tail probabilities of the distribution of the OLS estimator. These are useful for inference. Simulations for mediumsized samples reveal considerable deviations of the coefficient estimates from their true values, in line with our theoretical formulas. The formulas provide a benchmark for judging the observed highly variable cross country estimates of the expectations coefficient in yield curve regressions. ",
author = "Mikosch, {Thomas Valentin} and {de Vries}, Casper",
year = "2013",
doi = "10.1016/j.jeconom.2012.08.015",
language = "English",
volume = "172",
pages = "205--221",
journal = "Journal of Econometrics",
issn = "0304-4076",
publisher = "Elsevier",
number = "2",

}

RIS

TY - JOUR

T1 - Heavy tails of OLS

AU - Mikosch, Thomas Valentin

AU - de Vries, Casper

PY - 2013

Y1 - 2013

N2 - Suppose the tails of the noise distribution in a regression exhibit power law behavior. Then thedistribution of the OLS regression estimator inherits this tail behavior. This is relevant for regressions involving financial data. We derive explicit finite sample expressions for the tail probabilities of the distribution of the OLS estimator. These are useful for inference. Simulations for mediumsized samples reveal considerable deviations of the coefficient estimates from their true values, in line with our theoretical formulas. The formulas provide a benchmark for judging the observed highly variable cross country estimates of the expectations coefficient in yield curve regressions.

AB - Suppose the tails of the noise distribution in a regression exhibit power law behavior. Then thedistribution of the OLS regression estimator inherits this tail behavior. This is relevant for regressions involving financial data. We derive explicit finite sample expressions for the tail probabilities of the distribution of the OLS estimator. These are useful for inference. Simulations for mediumsized samples reveal considerable deviations of the coefficient estimates from their true values, in line with our theoretical formulas. The formulas provide a benchmark for judging the observed highly variable cross country estimates of the expectations coefficient in yield curve regressions.

U2 - 10.1016/j.jeconom.2012.08.015

DO - 10.1016/j.jeconom.2012.08.015

M3 - Journal article

VL - 172

SP - 205

EP - 221

JO - Journal of Econometrics

JF - Journal of Econometrics

SN - 0304-4076

IS - 2

ER -

ID: 46001523