Bayesian Analysis of Latent Threshold Dynamic Models

Journal Article (Journal Article)

We discuss a general approach to dynamic sparsity modeling in multivariate time series analysis. Time-varying parameters are linked to latent processes that are thresholded to induce zero values adaptively, providing natural mechanisms for dynamic variable inclusion/selection. We discuss Bayesian model specification, analysis and prediction in dynamic regressions, time-varying vector autoregressions, and multivariate volatility models using latent thresholding. Application to a topical macroeconomic time series problem illustrates some of the benefits of the approach in terms of statistical and economic interpretations as well as improved predictions. Supplementary materials for this article are available online. © 2013 Copyright Taylor and Francis Group, LLC.

Full Text

Duke Authors

Cited Authors

  • Nakajima, J; West, M

Published Date

  • April 1, 2013

Published In

Volume / Issue

  • 31 / 2

Start / End Page

  • 151 - 164

Electronic International Standard Serial Number (EISSN)

  • 1537-2707

International Standard Serial Number (ISSN)

  • 0735-0015

Digital Object Identifier (DOI)

  • 10.1080/07350015.2012.747847

Citation Source

  • Scopus