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Bayesian nonparametric methods for financial and macroeconomic time series analysis

Kalli, Maria (2020) Bayesian nonparametric methods for financial and macroeconomic time series analysis. In: Dortet-Bernadet, Jean-Luc and Fan, Yanan and Nott, David and Smith, Micheal, eds. Flexible Bayesian Regression Modelling. Elsevier - Academic Press, USA, pp. 91-120. ISBN 978-0-12-815862-3. (In press) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided)

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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
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: Faculties > Sciences > School of Mathematics Statistics and Actuarial Science > Statistics
Depositing User: Maria Kalli
Date Deposited: 13 Nov 2019 11:44 UTC
Last Modified: 14 Nov 2019 12:15 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/65796 (The current URI for this page, for reference purposes)
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