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Noisy News in Business Cycles

  • Mario Forni

    ()

  • Luca Gambetti

    ()

  • Marco Lippi

    ()

  • Luca Sala

    ()

The contribution of the present paper is twofold. First, we show that in a situation where agents can only observe a noisy signal of the shock to future economic fundamentals, the "noisy news", SVAR models can still be successfully employed to estimate the shock and the associated impulse response functions. Identification is reached by means of dynamic rotations of the reduced form residuals. Second, we use our identification approach to investigate the role of noise and news as sources of business cycle fluctuations. We find that noise shocks, the component of the signal unrelated to economic fundamentals, generate hump-shaped responses of GDP, consumption and investment and account for a third of their variance. Moreover, news and noise together account for more than half of the fluctuations in GDP, consumption and investment

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Paper provided by University of Modena and Reggio E., Dept. of Economics "Marco Biagi" in its series Center for Economic Research (RECent) with number 097.

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Length: pages 39
Date of creation: Mar 2014
Date of revision:
Handle: RePEc:mod:recent:097
Contact details of provider: Web page: http://www.recent.unimore.it/

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  4. Forni, Mario & Gambetti, Luca, 2014. "Sufficient information in structural VARs," Journal of Monetary Economics, Elsevier, vol. 66(C), pages 124-136.
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  8. Giannone, Domenico & Reichlin, Lucrezia, 2006. "Does information help recovering structural shocks from past observations?," Working Paper Series 0632, European Central Bank.
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  18. George-Marios Angeletos & Jennifer La'O, 2010. "Noisy Business Cycles," NBER Chapters, in: NBER Macroeconomics Annual 2009, Volume 24, pages 319-378 National Bureau of Economic Research, Inc.
  19. Forni, Mario & Giannone, Domenico & Lippi, Marco & Reichlin, Lucrezia, 2009. "Opening The Black Box: Structural Factor Models With Large Cross Sections," Econometric Theory, Cambridge University Press, vol. 25(05), pages 1319-1347, October.
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