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Estimating dynamic equilibrium models with stochastic volatility
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DOI：10.1016/j.jeconom.2014.08.010.png)
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This paper develops a particle filtering algorithm to estimate dynamic equilibrium models with stochastic volatility using a likelihood-based approach. The algorithm, which exploits the structure and profusion of shocks in stochastic volatility models, is versatile and computationally tractable even in large-scale models. As an application, we use our algorithm and Bayesian methods to estimate a business cycle model of the US economy with both stochastic volatility and parameter drifting in monetary policy. Our application shows the importance of stochastic volatility in accounting for the dynamics of the data. (C) 2014 Elsevier B.V. All rights reserved.
Keyword:
Dynamic equilibrium models
Stochastic volatility
Parameter drifting
Bayesian methods
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