Perera, I. and Silvapulle, M. (2023) Bootstrap specification tests for dynamic conditional distribution models. Journal of Econometrics, 235 (2). pp. 949-971. ISSN 0304-4076
Abstract
This paper proposes bootstrap based tests for the specification of a given parametric conditional distribution in autoregressive time series with GARCH-type disturbances. The tests are based on an estimated residual empirical process and are implemented by parametric bootstrap. We show that the proposed tests are asymptotically valid, consistent, and have nontrivial asymptotic power against a large proportion of local alternatives. Our approach relies on non-primitive regularity conditions and certain properties of exponential almost sure convergence. The regularity conditions are shown to be satisfied by GARCH(p,q); this technique of verification is applicable to other models as well. In our Monte Carlo study, the proposed tests performed well and better than several competing tests, including the information matrix test. A real data example illustrates the testing procedure.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 Elsevier B.V. This is an author produced version of a paper subsequently published in Journal of Econometrics. Uploaded in accordance with the publisher's self-archiving policy. Article available under the terms of the CC-BY-NC-ND licence (https://creativecommons.org/licenses/by-nc-nd/4.0/). |
Keywords: | GARCH; Goodness-of-fit; Residual empirical process; Kolmogorov–Smirnov test; Lack-of-fit test; Stochastic recurrence equations |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Social Sciences (Sheffield) > Department of Economics (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 26 Sep 2022 11:18 |
Last Modified: | 25 Sep 2024 11:49 |
Status: | Published |
Publisher: | Elsevier |
Refereed: | Yes |
Identification Number: | 10.1016/j.jeconom.2022.08.006 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:191208 |