An Introduction to Bootstrap Theory in Time Series Econometrics

Research output: Chapter in Book/Report/Conference proceedingBook chapterResearchpeer-review

Standard

An Introduction to Bootstrap Theory in Time Series Econometrics. / Cavaliere, Giuseppe; Nielsen, Heino Bohn; Rahbek, Anders.

Oxford Research Encyclopedia of Economics and Finance. ed. / Jonathan H. Hamilton; Avinash Dixit; Sebastian Edwards; Kenneth Judd. Oxford University Press, 2021.

Research output: Chapter in Book/Report/Conference proceedingBook chapterResearchpeer-review

Harvard

Cavaliere, G, Nielsen, HB & Rahbek, A 2021, An Introduction to Bootstrap Theory in Time Series Econometrics. in JH Hamilton, A Dixit, S Edwards & K Judd (eds), Oxford Research Encyclopedia of Economics and Finance. Oxford University Press. https://doi.org/10.1093/acrefore/9780190625979.013.493

APA

Cavaliere, G., Nielsen, H. B., & Rahbek, A. (2021). An Introduction to Bootstrap Theory in Time Series Econometrics. In J. H. Hamilton, A. Dixit, S. Edwards, & K. Judd (Eds.), Oxford Research Encyclopedia of Economics and Finance Oxford University Press. https://doi.org/10.1093/acrefore/9780190625979.013.493

Vancouver

Cavaliere G, Nielsen HB, Rahbek A. An Introduction to Bootstrap Theory in Time Series Econometrics. In Hamilton JH, Dixit A, Edwards S, Judd K, editors, Oxford Research Encyclopedia of Economics and Finance. Oxford University Press. 2021 https://doi.org/10.1093/acrefore/9780190625979.013.493

Author

Cavaliere, Giuseppe ; Nielsen, Heino Bohn ; Rahbek, Anders. / An Introduction to Bootstrap Theory in Time Series Econometrics. Oxford Research Encyclopedia of Economics and Finance. editor / Jonathan H. Hamilton ; Avinash Dixit ; Sebastian Edwards ; Kenneth Judd. Oxford University Press, 2021.

Bibtex

@inbook{8ce726b9676c4347acdb31b51fe7f14d,
title = "An Introduction to Bootstrap Theory in Time Series Econometrics",
abstract = "While often simple to implement in practice, application of the bootstrap in econometric modeling of economic and financial time series requires establishing validity of the bootstrap. Establishing bootstrap asymptotic validity relies on verifying often nonstandard regularity conditions. In particular, bootstrap versions of classic convergence in probability and distribution, and hence of laws of large numbers and central limit theorems, are critical ingredients. Crucially, these depend on the type of bootstrap applied (e.g., wild or independently and identically distributed (i.i.d.) bootstrap) and on the underlying econometric model and data. Regularity conditions and their implications for possible improvements in terms of (empirical) size and power for bootstrap-based testing differ from standard asymptotic testing, which can be illustrated by simulations.",
keywords = "Faculty of Social Sciences, bootstrap, bootstrap validity, bootstrap convergence, weak convergence in probability, asymptotic theory, bootstrap asymptotics",
author = "Giuseppe Cavaliere and Nielsen, {Heino Bohn} and Anders Rahbek",
year = "2021",
doi = "10.1093/acrefore/9780190625979.013.493",
language = "English",
editor = "Hamilton, {Jonathan H.} and Avinash Dixit and Sebastian Edwards and Kenneth Judd",
booktitle = "Oxford Research Encyclopedia of Economics and Finance",
publisher = "Oxford University Press",
address = "United Kingdom",

}

RIS

TY - CHAP

T1 - An Introduction to Bootstrap Theory in Time Series Econometrics

AU - Cavaliere, Giuseppe

AU - Nielsen, Heino Bohn

AU - Rahbek, Anders

PY - 2021

Y1 - 2021

N2 - While often simple to implement in practice, application of the bootstrap in econometric modeling of economic and financial time series requires establishing validity of the bootstrap. Establishing bootstrap asymptotic validity relies on verifying often nonstandard regularity conditions. In particular, bootstrap versions of classic convergence in probability and distribution, and hence of laws of large numbers and central limit theorems, are critical ingredients. Crucially, these depend on the type of bootstrap applied (e.g., wild or independently and identically distributed (i.i.d.) bootstrap) and on the underlying econometric model and data. Regularity conditions and their implications for possible improvements in terms of (empirical) size and power for bootstrap-based testing differ from standard asymptotic testing, which can be illustrated by simulations.

AB - While often simple to implement in practice, application of the bootstrap in econometric modeling of economic and financial time series requires establishing validity of the bootstrap. Establishing bootstrap asymptotic validity relies on verifying often nonstandard regularity conditions. In particular, bootstrap versions of classic convergence in probability and distribution, and hence of laws of large numbers and central limit theorems, are critical ingredients. Crucially, these depend on the type of bootstrap applied (e.g., wild or independently and identically distributed (i.i.d.) bootstrap) and on the underlying econometric model and data. Regularity conditions and their implications for possible improvements in terms of (empirical) size and power for bootstrap-based testing differ from standard asymptotic testing, which can be illustrated by simulations.

KW - Faculty of Social Sciences

KW - bootstrap

KW - bootstrap validity

KW - bootstrap convergence

KW - weak convergence in probability

KW - asymptotic theory

KW - bootstrap asymptotics

U2 - 10.1093/acrefore/9780190625979.013.493

DO - 10.1093/acrefore/9780190625979.013.493

M3 - Book chapter

BT - Oxford Research Encyclopedia of Economics and Finance

A2 - Hamilton, Jonathan H.

A2 - Dixit, Avinash

A2 - Edwards, Sebastian

A2 - Judd, Kenneth

PB - Oxford University Press

ER -

ID: 248288328