Publication Type
Journal Article
Version
publishedVersion
Publication Date
7-2023
Abstract
This article provides results on the validity of bootstrap inference methods for two-stage quasi-maximum likelihood estimation involving time series data, such as those used for multivariate volatility models or copula-based models. Existing approaches require the researcher to compute and combine many first- and second-order derivatives, which can be difficult to do and is susceptible to error. Bootstrap methods are simpler to apply, allowing the substitution of capital (CPU cycles) for labor (keeping track of derivatives). We show the consistency of the bootstrap distribution and consistency of bootstrap variance estimators, thereby justifying the use of bootstrap percentile intervals and bootstrap standard errors.
Keywords
Copulas, inference, multivariate GARCH
Discipline
Econometrics | Finance
Research Areas
Econometrics
Publication
Journal of Business and Economic Statistics
Volume
41
Issue
3
First Page
683
Last Page
694
ISSN
0735-0015
Identifier
10.1080/07350015.2022.2058949
Publisher
Taylor and Francis Group
Citation
GONÇALVES, Sílvia; HOUNYO, Ulrich; PATTON, Andrew John; and SHEPPARD, Kevin.
Bootstrapping two-stage quasi-maximum likelihood estimators of time series models. (2023). Journal of Business and Economic Statistics. 41, (3), 683-694.
Available at: https://ink.library.smu.edu.sg/soe_research/2884
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1080/07350015.2022.2058949