An Introduction to Bootstrap Theory in Time Series Econometrics
Research output: Chapter in Book/Report/Conference proceeding › Book chapter › Research › peer-review
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.
|Title of host publication||Oxford Research Encyclopedia of Economics and Finance|
|Editors||Jonathan H. Hamilton, Avinash Dixit, Sebastian Edwards, Kenneth Judd|
|Publisher||Oxford University Press|
|Publication status||Published - 2021|
- Faculty of Social Sciences - bootstrap, bootstrap validity, bootstrap convergence, weak convergence in probability, asymptotic theory, bootstrap asymptotics