Working Paper: CEPR ID: DP9130
Authors: Jess Fernández-Villaverde; Pablo A. Guerrón-Quintana; Juan Francisco Rubio-Ramirez
Abstract: We propose a novel method to estimate dynamic equilibrium models with stochastic volatility. First, we characterize the properties of the solution to this class of models. Second, we take advantage of the results about the structure of the solution to build a sequential Monte Carlo algorithm to evaluate the likelihood function of the model. The approach, which exploits the profusion of shocks in stochastic volatility models, is versatile and computationally tractable even in large-scale models, such as those often employed by policy-making institutions. 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.
Keywords: Bayesian methods; Dynamic equilibrium models; Parameter drifting; Stochastic volatility
JEL Codes: C11; E10; E30
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| stochastic volatility (C58) | dynamics of U.S. aggregate data (E19) |
| stochastic volatility (C58) | economic fluctuations (E32) |
| parameter drifting in monetary policy (E49) | dynamics of U.S. aggregate data (E19) |
| stochastic volatility + parameter drifting in monetary policy (C54) | successful economic model (P17) |
| volatility shocks (E32) | economic modeling (R15) |
| high volatility periods + weak monetary policy responses (E63) | economic dynamics in the 1970s (E65) |
| positive structural shocks + low volatility (E32) | economic dynamics in the 1990s (E65) |