Perera, I. and Silvapulle, M.J. (2021) Bootstrap based probability forecasting in multiplicative error models. Journal of Econometrics, 221 (1). pp. 1-24. ISSN 0304-4076
Abstract
As evidenced by an extensive empirical literature, multiplicative error models (MEM) show good performance in capturing the stylized facts of nonnegative time series; examples include, trading volume, financial durations, and volatility. This paper develops a bootstrap based method for producing multi-step-ahead probability forecasts for a nonnegative valued time-series obeying a parametric MEM. In order to test the adequacy of the underlying parametric model, a class of bootstrap specification tests is also developed. Rigorous proofs are provided for establishing the validity of the proposed bootstrap methods. The paper also establishes the validity of a bootstrap based method for producing probability forecasts in a class of semiparametric MEMs. Monte Carlo simulations suggest that our methods perform well in finite samples. A real data example illustrates the methods.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 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: | Multiplicative error model; Bootstrap; Probability forecast; Goodness-of-fit; Multi-step forecast |
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: | 03 Mar 2020 16:59 |
Last Modified: | 03 Mar 2022 01:38 |
Status: | Published |
Publisher: | Elsevier BV |
Refereed: | Yes |
Identification Number: | 10.1016/j.jeconom.2020.01.022 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:158018 |