Macroeconomics and Volatility: Data, Models, and Estimation
Jesus Fernandez-Villaverde and
Juan F Rubio-Ramirez
No 16618, NBER Working Papers from National Bureau of Economic Research, Inc
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.
JEL-codes: C01 C22 E10 (search for similar items in EconPapers)
Date: 2010-12
Note: EFG
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (21)
Published as (2013) \Macroeconomics and Volatility: Data, Models, and Methods." Joint with Juan F. Rubio-Ramrez (Duke University). In Advances in Economics and Econo- metrics: Theory and Applications, Tenth World Congress of the Econometric So- ciety , Cambridge University Press.
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Working Paper: Macroeconomics and Volatility: Data, Models, and Estimation (2010) 
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