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, . ISSN 1618-2510. (doi:10.1007/s10260-017-0384-0) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

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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: Faculties > Sciences > School of Mathematics Statistics and Actuarial Science
Faculties > Sciences > School of Mathematics Statistics and Actuarial Science > Statistics
Depositing User: Jim Griffin
Date Deposited: 10 Aug 2017 08:27 UTC
Last Modified: 29 May 2019 19:18 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
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