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Bayesian nonparametric methods for financial and macroeconomic time series analysis

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Original languageEnglish
Title of host publicationFlexible Bayesian Regression Modelling
EditorsMichael Smith , David Nott, Yanan Fan, Jean Luc Dorset Bernadette
PublisherElsevier
Chapter4
Number of pages30
Edition1
ISBN (Electronic)9780128158630
ISBN (Print)9780128158623
Published1 Jan 2020

King's Authors

Abstract

In this chapter we discuss the use of Bayesian nonparametric methods for time series analysis. First developed by [Freguson (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 macroeconomic time series, and demonstrate that volatility, long memory and vector autoregressive models underpinned by Bayesian nonparametric methods have superior out-of-sample predictive performance compared to other competitive models.

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