Bayesian nonparametric methods for financial and macroeconomic time series analysis

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.

• Publication year

  2020

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  Unknown venue

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• Fields of study

  Computer Science, Economics

• Identifiers
  DOI 10.1016/b978-0-12-815862-3.00012-3
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  Open on Semantic Scholar

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  Semantic Scholar

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