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News, Noise, and Fluctuations: An Empirical Exploration

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

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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File URL: http://www.nber.org/papers/w15015.pdf
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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
Note: EFG ME
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  1. Francesco Lippi & Stefano Neri, 2004. "Information variables for monetary policy in a small structural model of the euro area," Temi di discussione (Economic working papers) 511, Bank of Italy, Economic Research and International Relations Area.
  2. 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.
  3. Guido Lorenzoni, 2006. "A Theory of Demand Shocks," NBER Working Papers 12477, National Bureau of Economic Research, Inc.
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  8. Mark Aguiar & Gita Gopinath, 2007. "Emerging Market Business Cycles: The Cycle Is the Trend," Journal of Political Economy, University of Chicago Press, vol. 115, pages 69-102.
  9. 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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  11. Sims, Christopher A. & Zha, Tao, 2006. "Does Monetary Policy Generate Recessions?," Macroeconomic Dynamics, Cambridge University Press, vol. 10(02), pages 231-272, April.
  12. Richard Blundell & Ian Preston, 1997. "Consumption, inequality and income uncertainty," IFS Working Papers W97/15, Institute for Fiscal Studies.
  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. Robert B. Barsky & Eric R. Sims, 2012. "Information, Animal Spirits, and the Meaning of Innovations in Consumer Confidence," American Economic Review, American Economic Association, vol. 102(4), pages 1343-77, June.
  15. Emine Boz & Christian Daude & Ceyhun Bora Durdu, 2008. "Emerging market business cycles revisited: learning about the trend," International Finance Discussion Papers 927, Board of Governors of the Federal Reserve System (U.S.).
  16. Lippi, Francesco & Neri, Stefano, 2007. "Information variables for monetary policy in an estimated structural model of the euro area," Journal of Monetary Economics, Elsevier, vol. 54(4), pages 1256-1270, May.
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