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Bayesian nonparametric methods for financial and macroeconomic time series analysis

Kalli, Maria (2019) Bayesian nonparametric methods for financial and macroeconomic time series analysis. In: Fan, Yanan and Nott, David and Smith, Mike S. and Dortet-Bernadet, Jean-Luc, eds. Flexible Bayesian Regression Modelling. Elsevier, USA, pp. 91-120. ISBN 978-0-12-815862-3. E-ISBN 978-0-12-815863-0. (doi:10.1016/B978-0-12-815862-3.00012-3) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided) (KAR id:65796)

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Abstract

In this chapter we discuss the use of Bayesian nonparametric methods for time series anal- ysis. First developed by Ferguson (1973) these methods focus on how a stochastic process can be used as a prior over probability measures as well as a prior on the underlining mixing measure in a mixture model. The empirical examples of the chapter centre on financial and macroeco- nomic time series, and demonstrate that volatility, long memory and vector autoregressive models underpinned by Bayesian nonparametric methods have superior out-of-sample pre- dictive performance compared to other competitive models.

Item Type: Book section
DOI/Identification number: 10.1016/B978-0-12-815862-3.00012-3
Uncontrolled keywords: Stick-breaking processes, infinite mixtures, mixtures of experts, financial time series, macroeconomic time series
Subjects: Q Science > QA Mathematics (inc Computing science) > QA273 Probabilities
Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science
Depositing User: Maria Kalli
Date Deposited: 13 Nov 2019 11:44 UTC
Last Modified: 16 Feb 2021 13:52 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/65796 (The current URI for this page, for reference purposes)
Kalli, Maria: https://orcid.org/0000-0002-2492-4123
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