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Macroeconomics and Volatility: Data, Models, and Estimation

Author

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  • Jesús Fernández-Villaverde
  • Juan Rubio-Ramírez
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.

Suggested Citation

  • Jesús Fernández-Villaverde & Juan Rubio-Ramírez, 2010. "Macroeconomics and Volatility: Data, Models, and Estimation," NBER Working Papers 16618, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:16618
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    Cited by:

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    2. Philippe Mueller & Andrea Vedolin & Hao Zhou, 2019. "Short-Run Bond Risk Premia," Quarterly Journal of Finance (QJF), World Scientific Publishing Co. Pte. Ltd., vol. 9(03), pages 1-34, September.
    3. Markus K. Brunnermeier & Yuliy Sannikov, 2016. "Macro, Money and Finance: A Continuous Time Approach," NBER Working Papers 22343, National Bureau of Economic Research, Inc.
    4. Haroon Mumtaz & Konstantinos Theodoridis, 2015. "The International Transmission Of Volatility Shocks: An Empirical Analysis," Journal of the European Economic Association, European Economic Association, vol. 13(3), pages 512-533, June.
    5. Dario Caldara & Jesus Fernandez-Villaverde & Juan Rubio-Ramirez & Wen Yao, 2012. "Computing DSGE Models with Recursive Preferences and Stochastic Volatility," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 15(2), pages 188-206, April.
    6. Steffen Henzel & Elisabeth Wieland, 2013. "Synchronization and Changes in International Inflation Uncertainty," CESifo Working Paper Series 4194, CESifo.
    7. Frank Schorfheide, 2011. "Estimation and evaluation of DSGE models: progress and challenges," Working Papers 11-7, Federal Reserve Bank of Philadelphia.
    8. Gonzalez-Astudillo, Manuel, 2011. "Policy Rule Coefficients Driven by Latent Factors: Monetary and Fiscal Policy Interactions in an Endowment Economy," MPRA Paper 29976, University Library of Munich, Germany.
    9. Hall, Jamie, 2012. "Consumption dynamics in general equilibrium," MPRA Paper 43933, University Library of Munich, Germany.
    10. Brunnermeier, M.K. & Sannikov, Y., 2016. "Macro, Money, and Finance," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 1497-1545, Elsevier.
    11. Luis Ceballos & Damián Romero, 2014. "Risk Matters: The Impact of Nominal Uncertainty in Chile," Working Papers Central Bank of Chile 741, Central Bank of Chile.
    12. Kukacka, Jiri & Barunik, Jozef, 2017. "Estimation of financial agent-based models with simulated maximum likelihood," Journal of Economic Dynamics and Control, Elsevier, vol. 85(C), pages 21-45.
    13. Matteo Cacciatore & Federico Ravenna, 2021. "Uncertainty, Wages and the Business Cycle," The Economic Journal, Royal Economic Society, vol. 131(639), pages 2797-2823.
    14. Gorodnichenko, Yuriy & Ng, Serena, 2017. "Level and volatility factors in macroeconomic data," Journal of Monetary Economics, Elsevier, vol. 91(C), pages 52-68.
    15. Azzimonti, Marina & Talbert, Matthew, 2014. "Polarized business cycles," Journal of Monetary Economics, Elsevier, vol. 67(C), pages 47-61.
    16. Paolo Guarda & Abdelaziz Rouabah, 2015. "Is the financial sector Luxembourg?s engine of growth?," BCL working papers 97, Central Bank of Luxembourg.
    17. Steffen R. Henzel & Malte Rengel, 2017. "Dimensions Of Macroeconomic Uncertainty: A Common Factor Analysis," Economic Inquiry, Western Economic Association International, vol. 55(2), pages 843-877, April.
    18. Martín Uribe, 2011. "Comment on "Risk, Monetary Policy and the Exchange Rate"," NBER Chapters, in: NBER Macroeconomics Annual 2011, Volume 26, pages 315-324, National Bureau of Economic Research, Inc.
    19. Marina Azzimonti-Renzo, 2013. "The political polarization index," Working Papers 13-41, Federal Reserve Bank of Philadelphia.
    20. Lan, Hong & Meyer-Gohde, Alexander, 2013. "Solving DSGE models with a nonlinear moving average," Journal of Economic Dynamics and Control, Elsevier, vol. 37(12), pages 2643-2667.
    21. repec:hum:wpaper:sfb649dp2011-087 is not listed on IDEAS
    22. Dennis, Wesselbaum, 2012. "Stochastic Volatility in the U.S. Labor Market," MPRA Paper 43054, University Library of Munich, Germany.

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    More about this item

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • E10 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - General

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