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Sequentially Adaptive Bayesian Learning for a Nonlinear Model of the Secular and Cyclical Behavior of US Real GDP

Economics Discipline Group, School of Business, University of Technology Sydney, 14 - 28 Ultimo Road, Ultimo, NSW 2007, Australia
Academic Editors: Herman K. van Dijk, Francesco Ravazzolo, Nalan Basturk and Roberto Casarin
Econometrics 2016, 4(1), 10; https://doi.org/10.3390/econometrics4010010
Received: 1 October 2015 / Revised: 4 December 2015 / Accepted: 2 February 2016 / Published: 2 March 2016
(This article belongs to the Special Issue Computational Complexity in Bayesian Econometric Analysis)
There is a one-to-one mapping between the conventional time series parameters of a third-order autoregression and the more interpretable parameters of secular half-life, cyclical half-life and cycle period. The latter parameterization is better suited to interpretation of results using both Bayesian and maximum likelihood methods and to expression of a substantive prior distribution using Bayesian methods. The paper demonstrates how to approach both problems using the sequentially adaptive Bayesian learning algorithm and sequentially adaptive Bayesian learning algorithm (SABL) software, which eliminates virtually of the substantial technical overhead required in conventional approaches and produces results quickly and reliably. The work utilizes methodological innovations in SABL including optimization of irregular and multimodal functions and production of the conventional maximum likelihood asymptotic variance matrix as a by-product. View Full-Text
Keywords: business cycles; posterior simulation; sequential Monte Carlo business cycles; posterior simulation; sequential Monte Carlo
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Geweke, J. Sequentially Adaptive Bayesian Learning for a Nonlinear Model of the Secular and Cyclical Behavior of US Real GDP. Econometrics 2016, 4, 10.

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