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Surprise and uncertainty indexes: real-time aggregation of real-activity macro surprises

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  • Chiara Scotti

Abstract

I construct two real-time, real activity indexes: (i) a surprise index that summarizes recent economic data surprises and measures optimism/pessimism about the state of the economy, and (ii) an uncertainty index that measures uncertainty related to the state of the economy. The indexes, on a given day, are weighted averages of the surprises or squared surprises from a set of macro releases, where the weights depend on the contribution of the associated real activity indicator to a business condition index a la Aruoba, Diebold, and Scotti (2009). I construct indexes for the United States, Euro Area, the United Kingdom, Canada, Japan. I show that the surprise index preserves the properties of the underlying series in affecting asset prices, with the advantage of being a parsimonious summary measure of real-activity surprises. For the United States, I present the real-activity uncertainty index in relation to other proxies commonly used to measure uncertainty and compare their macroeconomic impact. I find evidence that when uncertainty is strictly related to real activity it has a potentially milder impact on economic activity than when it also relates to the financial sector.

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Bibliographic Info

Paper provided by Board of Governors of the Federal Reserve System (U.S.) in its series International Finance Discussion Papers with number 1093.

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Date of creation: 2013
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Handle: RePEc:fip:fedgif:1093

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  1. Sylvain Leduc & Zheng Liu, 2012. "Uncertainty shocks are aggregate demand shocks," Working Paper Series 2012-10, Federal Reserve Bank of San Francisco.
  2. Giannone, Domenico & Reichlin, Lucrezia & Small, David, 2008. "Nowcasting: The real-time informational content of macroeconomic data," Journal of Monetary Economics, Elsevier, vol. 55(4), pages 665-676, May.
  3. Bańbura, Marta & Modugno, Michele, 2010. "Maximum likelihood estimation of factor models on data sets with arbitrary pattern of missing data," Working Paper Series 1189, European Central Bank.
  4. Doz, Catherine & Giannone, Domenico & Reichlin, Lucrezia, 2006. "A quasi maximum likelihood approach for large approximate dynamic factor models," Working Paper Series 0674, European Central Bank.
  5. Doz, Catherine & Giannone, Domenico & Reichlin, Lucrezia, 2011. "A two-step estimator for large approximate dynamic factor models based on Kalman filtering," Journal of Econometrics, Elsevier, vol. 164(1), pages 188-205, September.
  6. Ana Fostel & John Geanakoplos, 2010. "Why Does Bad News Increase Volatility and Decrease Leverage?," Cowles Foundation Discussion Papers 1762, Cowles Foundation for Research in Economics, Yale University.
  7. Pesaran, H. Hashem & Shin, Yongcheol, 1998. "Generalized impulse response analysis in linear multivariate models," Economics Letters, Elsevier, vol. 58(1), pages 17-29, January.
  8. Faust, Jon & Rogers, John H & Wright, Jonathan H, 2005. "News and Noise in G-7 GDP Announcements," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 37(3), pages 403-19, June.
  9. Camacho, Maximo & Pérez-Quirós, Gabriel, 2009. "Introducing the Euro-STING: Short-Term Indicator of Euro Area Growth," CEPR Discussion Papers 7343, C.E.P.R. Discussion Papers.
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Cited by:
  1. Gitanjali Kumar, 2013. "High-Frequency Real Economic Activity Indicator for Canada," Working Papers 13-42, Bank of Canada.
  2. Nicholas Bloom, 2014. "Fluctuations In Uncertainty," Working Papers 14-17, Center for Economic Studies, U.S. Census Bureau.
  3. Kyle Jurado & Sydney C. Ludvigson & Serena Ng, 2013. "Measuring Uncertainty," NBER Working Papers 19456, National Bureau of Economic Research, Inc.
  4. Amélie Charles & Olivier Darné & Laurent Ferrara, 2014. "Does the Great Recession imply the end of the Great Moderation? International evidence," EconomiX Working Papers 2014-21, University of Paris West - Nanterre la Défense, EconomiX.

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