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Discussion of “Nonparametric Bayesian Inference in Applications”: Bayesian nonparametric methods in econometrics

Griffin, Jim E., Kalli, Maria, Steel, Mark (2017) Discussion of “Nonparametric Bayesian Inference in Applications”: Bayesian nonparametric methods in econometrics. Statistical Methods & Applications, 27 (2). pp. 207-218. ISSN 1618-2510. (doi:10.1007/s10260-017-0384-0) (KAR id:62600)

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Official URL
https://doi.org/10.1007/s10260-017-0384-0

Abstract

The use of Bayesian nonparametrics models has increased rapidly over the last few decades driven by increasing computational power and the development of efficient Markov chain Monte Carlo algorithms. We review some applications of these models in economic applications including: volatility modelling (using both stochastic volatility models and GARCH-type models) with Dirichlet process mixture models, uses in portfolio allocation problems, long memory models with flexible forms of time-dependence, flexible extension of the dynamic Nelson-Siegel model for interest rate yields and multivariate time series models used in macroeconometrics.

Item Type: Article
DOI/Identification number: 10.1007/s10260-017-0384-0
Uncontrolled keywords: Dirichlet process; Normalized random measures with independent increments; Volatility; Infinite mixture model; Interest rates; Portfolio allocation; Long memory; Time series
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science
Depositing User: Jim Griffin
Date Deposited: 10 Aug 2017 08:27 UTC
Last Modified: 16 Feb 2021 13:47 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/62600 (The current URI for this page, for reference purposes)
Griffin, Jim E.: https://orcid.org/0000-0002-4828-7368
Kalli, Maria: https://orcid.org/0000-0002-2492-4123
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