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Macroeconomics and Volatility: Data, Models, and Estimation

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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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    References listed on IDEAS

    as
    1. Jesús Fernández-Villaverde & Juan F. Rubio-Ramírez, 2007. "Estimating Macroeconomic Models: A Likelihood Approach," Review of Economic Studies, Oxford University Press, vol. 74(4), pages 1059-1087.
    2. Bekaert, Geert & Hoerova, Marie & Lo Duca, Marco, 2013. "Risk, uncertainty and monetary policy," Journal of Monetary Economics, Elsevier, vol. 60(7), pages 771-788.
    3. Jesus Fernández-Villaverde & Pablo Guerrón-Quintana & Juan F. Rubio-Ramírez., 2010. "Reading the recent monetary history of the U.S., 1959-2007," Working Papers 10-15, Federal Reserve Bank of Philadelphia.
    4. Juan Rubio-Ramirez & Jesus Fernandez-Villaverde & Pablo A. Guerron-Quintana, 2010. "Fortune or Virtue: Time Variant Volatilities versus Parameter Drifting in U.S. Data," 2010 Meeting Papers 270, Society for Economic Dynamics.
    5. Jesus Fernandez-Villaverde & Pablo Guerron-Quintana & Juan F. Rubio-Ramirez & Martin Uribe, 2011. "Risk Matters: The Real Effects of Volatility Shocks," American Economic Review, American Economic Association, vol. 101(6), pages 2530-2561, October.
    6. Juan F. Rubio-Ramirez & Jesus Fernández-Villaverde, 2005. "Estimating dynamic equilibrium economies: linear versus nonlinear likelihood," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(7), pages 891-910.
    7. Manuel S. Santos & Adrian Peralta-Alva, 2005. "Accuracy of Simulations for Stochastic Dynamic Models," Econometrica, Econometric Society, vol. 73(6), pages 1939-1976, November.
    8. Graciela L. Kaminsky & Carmen M. Reinhart & Carlos A. Végh, 2003. "The Unholy Trinity of Financial Contagion," Journal of Economic Perspectives, American Economic Association, vol. 17(4), pages 51-74, Fall.
    9. Kiseok Lee & Shawn Ni & Ronald A. Ratti, 1995. "Oil Shocks and the Macroeconomy: The Role of Price Variability," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4), pages 39-56.
    10. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    11. R?diger Bachmann & Steffen Elstner & Eric R. Sims, 2013. "Uncertainty and Economic Activity: Evidence from Business Survey Data," American Economic Journal: Macroeconomics, American Economic Association, vol. 5(2), pages 217-249, April.
    12. Jesus Fernández-Villaverde & Pablo Guerrón-Quintana & Juan F. Rubio-Ramírez, 2010. "Fortune or virtue: time-variant volatilities versus parameter drifting," Working Papers 10-14, Federal Reserve Bank of Philadelphia.
    13. Elder, John, 2004. "Another Perspective on the Effects of Inflation Uncertainty," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 36(5), pages 911-928, October.
    14. Kevin B. Grier & Mark J. Perry, 2000. "The effects of real and nominal uncertainty on inflation and output growth: some garch-m evidence," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(1), pages 45-58.
    15. Fernandez-Villaverde, Jesus & Francisco Rubio-Ramirez, Juan, 2004. "Comparing dynamic equilibrium models to data: a Bayesian approach," Journal of Econometrics, Elsevier, vol. 123(1), pages 153-187, November.
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    Citations

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    Cited by:

    1. Fabrice Collard & Sujoy Mukerji & Kevin Sheppard & Jean-Marc Tallon, 2011. "Ambiguity and the historical equity premium," Documents de travail du Centre d'Economie de la Sorbonne 11032, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    2. Azzimonti, Marina & Talbert, Matthew, 2014. "Polarized business cycles," Journal of Monetary Economics, Elsevier, vol. 67(C), pages 47-61.
    3. 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.
    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. 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.
    6. Philippe Mueller & Andrea Vedolin & Hao Zhou, 2011. "Short Run Bond Risk Premia," FMG Discussion Papers dp686, Financial Markets Group.
    7. 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.
    8. Steffen Henzel & Elisabeth Wieland, 2013. "Synchronization and Changes in International Inflation Uncertainty," CESifo Working Paper Series 4194, CESifo Group Munich.
    9. Marina Azzimonti-Renzo, 2013. "The political polarization index," Working Papers 13-41, Federal Reserve Bank of Philadelphia.
    10. 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.
    11. repec:eee:macchp:v2-1497 is not listed on IDEAS
    12. repec:eee:dyncon:v:85:y:2017:i:c:p:21-45 is not listed on IDEAS
    13. Dennis, Wesselbaum, 2012. "Stochastic Volatility in the U.S. Labor Market," MPRA Paper 43054, University Library of Munich, Germany.
    14. 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.
    15. repec:eee:moneco:v:91:y:2017:i:c:p:52-68 is not listed on IDEAS
    16. 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.
    17. Gorodnichenko, Yuriy & Ng, Serena, 2017. "Level and volatility factors in macroeconomic data," Journal of Monetary Economics, Elsevier, vol. 91(C), pages 52-68.

    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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