Working Paper: NBER ID: w16618
Authors: Jess Fernández-Villaverde; Juan Rubio-RamÃrez
Abstract: One basic feature of aggregate data is the presence of time-varying variance in real and nominal variables. Periods of high volatility are followed by periods of low volatility. For instance, the turbulent 1970s were followed by the much more tranquil times of the great moderation from 1984 to 2007. Modeling these movements in volatility is important to understand the source of aggregate fluctuations, the evolution of the economy, and for policy analysis. In this chapter, we first review the different mechanisms proposed in the literature to generate changes in volatility similar to the ones observed in the data. Second, we document the quantitative importance of time-varying volatility in aggregate time series. Third, we present a prototype business cycle model with time-varying volatility and explain how it can be computed and how it can be taken to the data using likelihood-based methods and non-linear filtering theory. Fourth, we present two "real life" applications. We conclude by summarizing what we know and what we do not know about volatility in macroeconomics and by pointing out some directions for future research.
Keywords: macroeconomics; volatility; DSGE models; time-varying variance
JEL Codes: C01; C22; E10
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| time-varying volatility (C22) | aggregate fluctuations (E10) |
| increased volatility (E32) | changes in economic dynamics (F69) |
| reduced volatility (great moderation) (E32) | improved inventory control (M11) |
| reduced volatility (great moderation) (E32) | changes in monetary policy (E52) |
| volatility shocks (E32) | investment decisions (G11) |
| volatility shocks (E32) | delay in purchases (D15) |
| stochastic volatility (C58) | better fitting of DSGE models (E13) |
| constant volatility (C69) | poor fitting of data (C52) |