An Introduction to Bootstrap Theory in Time Series Econometrics
Research output: Chapter in Book/Report/Conference proceeding › Book chapter › Research › peer-review
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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 proceeding › Book chapter › Research › peer-review
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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