Estimating Dynamic Equilibrium Models with Stochastic Volatility

• Autores: Jesús Fernández Villaverde, Pablo Guerrón Quintana, Juan F. Rubio Ramírez
• Localización: Documentos de trabajo ( FEDEA ), ISSN 1696-7496, Nº. 11, 2014, págs. 1-84
• Idioma: inglés
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• Resumen

  • This paper develops a particle .ltering 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 U.S. 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.