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News, Noise, and Fluctuations: An Empirical Exploration

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  • Olivier J. Blanchard
  • Jean-Paul L'Huillier
  • Guido Lorenzoni

Abstract

We explore empirically models of aggregate fluctuations with two basic ingredients: agents form anticipations about the future based on noisy sources of information; these anticipations affect spending and output in the short run. Our objective is to separate fluctuations due to actual changes in fundamentals (news) from those due to temporary errors in the private sector's estimates of these fundamentals (noise). Using a simple model where the consumption random walk hypothesis holds exactly, we address some basic methodological issues and take a first pass at the data. First, we show that if the econometrician has no informational advantage over the agents in the model, structural VARs cannot be used to identify news and noise shocks. Next, we develop a structural Maximum Likelihood approach which allows us to identify the model's parameters and to evaluate the role of news and noise shocks. Applied to postwar U.S. data, this approach suggests that noise shocks play an important role in short-run fluctuations.

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Paper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 15015.

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Date of creation: May 2009
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Publication status: published as Olivier J. Blanchard & Jean-Paul L'Huillier & Guido Lorenzoni, 2013. "News, Noise, and Fluctuations: An Empirical Exploration," American Economic Review, American Economic Association, vol. 103(7), pages 3045-70, December.
Handle: RePEc:nbr:nberwo:15015

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  1. Nir Jaimovich & Sergio Rebelo, 2009. "Can News about the Future Drive the Business Cycle?," American Economic Review, American Economic Association, vol. 99(4), pages 1097-1118, September.
  2. Galí, Jordi, 1996. "Technology, Employment, and the Business Cycle: Do Technology Shocks Explain Aggregate Fluctuations?," CEPR Discussion Papers 1499, C.E.P.R. Discussion Papers.
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  6. Sims, Christopher A. & Zha, Tao, 2006. "Does Monetary Policy Generate Recessions?," Macroeconomic Dynamics, Cambridge University Press, vol. 10(02), pages 231-272, April.
  7. Guido Lorenzoni, 2006. "A Theory of Demand Shocks," NBER Working Papers 12477, National Bureau of Economic Research, Inc.
  8. Todd B. Walker & Eric M. Leeper & Shu-Chun S. Yang, 2012. "Fiscal Foresight and Information Flows," IMF Working Papers 12/153, International Monetary Fund.
  9. Anderson, Evan W. & McGrattan, Ellen R. & Hansen, Lars Peter & Sargent, Thomas J., 1996. "Mechanics of forming and estimating dynamic linear economies," Handbook of Computational Economics, in: H. M. Amman & D. A. Kendrick & J. Rust (ed.), Handbook of Computational Economics, edition 1, volume 1, chapter 4, pages 171-252 Elsevier.
  10. Richard Blundell & Ian Preston, 1997. "Consumption, inequality and income uncertainty," IFS Working Papers W97/15, Institute for Fiscal Studies.
  11. Danny Quah, 1988. "The Relative Importance of Permanent and Transitory Components: Identification and Some Theoretical Bounds," Working papers 498, Massachusetts Institute of Technology (MIT), Department of Economics.
  12. Mark Aguiar & Gita Gopinath, 2004. "Emerging market business cycles: the cycle is the trend," Working Papers 04-4, Federal Reserve Bank of Boston.
  13. Eric M. Leeper & Todd B. Walker & Shu-Chun Susan Yang, 2011. "Foresight and Information Flows," NBER Working Papers 16951, National Bureau of Economic Research, Inc.
  14. Aguiar, Mark & Gopinath, Gita, 2007. "Emerging Market Business Cycles: The Cycle is the Trend," Scholarly Articles 11988098, Harvard University Department of Economics.
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