Stationary bootstrapping for cointegrating regressions

Dong Wan Shin; Eunju Hwang

doi:10.1016/j.spl.2012.10.007

Stationary bootstrapping for cointegrating regressions

Dong Wan Shin, Eunju Hwang

Department of Statistics

Research output: Contribution to journal › Article › peer-review

5 Scopus citations

Abstract

The validity of stationary bootstrapping is investigated for cointegrating regressions in large samples as well as in finite samples. The bootstrap ordinary least squares estimator (OLSE) is shown to be valid in large samples having the same limiting distribution as the OLSE under a similar normalization. Large sample validity of a bootstrap test regarding cointegration parameters is also established. Finite sample size and power properties of the bootstrap test are investigated via a Monte Carlo experiment.

Original language	English
Pages (from-to)	474-480
Number of pages	7
Journal	Statistics and Probability Letters
Volume	83
Issue number	2
DOIs	https://doi.org/10.1016/j.spl.2012.10.007
State	Published - Feb 2013

Keywords

Cointegrating regression
Stationary bootstrapping

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10.1016/j.spl.2012.10.007

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title = "Stationary bootstrapping for cointegrating regressions",

abstract = "The validity of stationary bootstrapping is investigated for cointegrating regressions in large samples as well as in finite samples. The bootstrap ordinary least squares estimator (OLSE) is shown to be valid in large samples having the same limiting distribution as the OLSE under a similar normalization. Large sample validity of a bootstrap test regarding cointegration parameters is also established. Finite sample size and power properties of the bootstrap test are investigated via a Monte Carlo experiment.",

keywords = "Cointegrating regression, Stationary bootstrapping",

author = "Shin, {Dong Wan} and Eunju Hwang",

note = "Funding Information: The authors are very grateful to an anonymous referee for valuable comments which improved the paper considerably. This research was supported by the National Research Foundation of Korea ( NRF-2009-0084772 , and NRF-2009-0070618 ). ",

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AU - Shin, Dong Wan

AU - Hwang, Eunju

N1 - Funding Information: The authors are very grateful to an anonymous referee for valuable comments which improved the paper considerably. This research was supported by the National Research Foundation of Korea ( NRF-2009-0084772 , and NRF-2009-0070618 ).

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Y1 - 2013/2

N2 - The validity of stationary bootstrapping is investigated for cointegrating regressions in large samples as well as in finite samples. The bootstrap ordinary least squares estimator (OLSE) is shown to be valid in large samples having the same limiting distribution as the OLSE under a similar normalization. Large sample validity of a bootstrap test regarding cointegration parameters is also established. Finite sample size and power properties of the bootstrap test are investigated via a Monte Carlo experiment.

AB - The validity of stationary bootstrapping is investigated for cointegrating regressions in large samples as well as in finite samples. The bootstrap ordinary least squares estimator (OLSE) is shown to be valid in large samples having the same limiting distribution as the OLSE under a similar normalization. Large sample validity of a bootstrap test regarding cointegration parameters is also established. Finite sample size and power properties of the bootstrap test are investigated via a Monte Carlo experiment.

KW - Cointegrating regression

KW - Stationary bootstrapping

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DO - 10.1016/j.spl.2012.10.007

M3 - Article

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SP - 474

EP - 480

JO - Statistics and Probability Letters

JF - Statistics and Probability Letters

IS - 2

ER -

Stationary bootstrapping for cointegrating regressions

Abstract

Keywords

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