Conferences at Department of Economics, University of Toronto, RCEF 2012: Cities, Open Economies, and Public Policy

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Bayesian analysis of latent threshold dynamic models

Jouchi Nakajima*, Mike West

Last modified: 2012-05-11


We describe a general approach to dynamic sparsity modelling in time series and state-space models. Time-varying parameters are linked to latent processes that are thresholded to induce zero values adaptively, providing dynamic variable inclusion/selection. We discuss Bayesian model estimation and prediction in dynamic regressions, time-varying vector autoregressions and multivariate volatility models using latent thresholding. Substantive examples in macroeconomics and financial time series show the utility of this approach to dynamic parameter reduction and time-varying sparsity modelling in terms of statistical and economic interpretations as well as improved predictions.

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