IDEAS home Printed from https://ideas.repec.org/
MyIDEAS: Log in (now much improved!) to save this paper

Dimensions of macroeconomic uncertainty: A common factor analysis

  • Steffen Henzel
  • Malte Rengel

In the current literature uncertainty about the future course of the economy is identified as a possible driver of business cycle fluctuations. In fact, uncertainty surrounds the movements of all economic variables which gives rise to a monitoring problem. We identify the different dimensions of uncertainty in the macroeconomy. To this end, weconstruct a large dataset covering all forms of economic uncertainty and unravel the fundamental factors that account for the common dynamics therein. These common factors are interpreted as macroeconomic uncertainty. Our results show that the first factor captures business cycle uncertainty while the second factor is identified as oil and commodity price uncertainty. Finally, we demonstrate that a distinction between both types of macroeconomic uncertainty is essential since they have rather different implications for economic activity.

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL: http://www.cesifo-group.de/portal/page/portal/DocBase_Content/WP/WP-Ifo_Working_Papers/wp-ifo-2013/IfoWorkingPaper-167.pdf
Download Restriction: no

Paper provided by Ifo Institute - Leibniz Institute for Economic Research at the University of Munich in its series Ifo Working Paper Series with number Ifo Working Paper No. 167.

as
in new window

Length:
Date of creation: 2013
Date of revision:
Handle: RePEc:ces:ifowps:_167
Contact details of provider: Postal:
Poschingerstr. 5, 81679 München

Phone: +49-89-9224-0
Fax: +49-89-985369
Web page: http://www.cesifo-group.de
Email:


More information through EDIRC

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

as in new window
  1. Goncalves, Silvia & Kilian, Lutz, 2004. "Bootstrapping autoregressions with conditional heteroskedasticity of unknown form," Journal of Econometrics, Elsevier, vol. 123(1), pages 89-120, November.
  2. 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.
  3. Domenico Giannone & Lucrezia Reichlin & Luca Sala, 2005. "Monetary Policy in Real Time," NBER Chapters, in: NBER Macroeconomics Annual 2004, Volume 19, pages 161-224 National Bureau of Economic Research, Inc.
  4. Benjamin Born & Johannes Pfeifer, 2011. "Policy Risk and the Business Cycle," Bonn Econ Discussion Papers bgse06_2011, University of Bonn, Germany.
  5. Forni, Mario & Gambetti, Luca, 2008. "The Dynamic Effects of Monetary Policy: A Structural Factor Model Approach," CEPR Discussion Papers 7098, C.E.P.R. Discussion Papers.
  6. Jesús Fernández-Villaverde & Pablo Guerrón-Quintana & Juan F. Rubio-Ramírez, 2010. "Fortune or Virtue: Time-Variant Volatilities Versus Parameter Drifting in U.S. Data," PIER Working Paper Archive 10-015, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
  7. Marta Bańbura & Michele Modugno, 2014. "Maximum Likelihood Estimation Of Factor Models On Datasets With Arbitrary Pattern Of Missing Data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(1), pages 133-160, 01.
  8. Geert Bekaert & Marie Hoerova & Marco Lo Duca, 2012. "Risk, uncertainty and monetary policy," Working Paper Research 229, National Bank of Belgium.
  9. John Elder & Apostolos Serletis, 2010. "Oil Price Uncertainty," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 42(6), pages 1137-1159, 09.
  10. Arthur F. Burns & Wesley C. Mitchell, 1946. "Measuring Business Cycles," NBER Books, National Bureau of Economic Research, Inc, number burn46-1, 08.
  11. Liesenfeld, Roman & Richard, Jean-Francois, 2003. "Univariate and multivariate stochastic volatility models: estimation and diagnostics," Journal of Empirical Finance, Elsevier, vol. 10(4), pages 505-531, September.
  12. Bundick, Brent & Basu, Susanto, 2014. "Uncertainty shocks in a model of effective demand," Research Working Paper RWP 14-15, Federal Reserve Bank of Kansas City, revised 01 Nov 2015.
  13. Lundbergh, Stefan & Teräsvirta, Timo, 1998. "Evaluating GARCH models," SSE/EFI Working Paper Series in Economics and Finance 292, Stockholm School of Economics, revised 03 May 1999.
  14. Nicholas Bloom, 2007. "The Impact of Uncertainty Shocks," NBER Working Papers 13385, National Bureau of Economic Research, Inc.
  15. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
  16. Nicholas Bloom & Max Floetotto & Nir Jaimovich & Itay Saporta-Eksten & Stephen J. Terry, 2012. "Really Uncertain Business Cycles," NBER Working Papers 18245, National Bureau of Economic Research, Inc.
  17. Edward S. Knotek II & Shujaat Khan, 2011. "How do households respond to uncertainty shocks?," Economic Review, Federal Reserve Bank of Kansas City, issue Q II.
  18. 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.
  19. Ramey, Garey & Ramey, Valerie A, 1995. "Cross-Country Evidence on the Link between Volatility and Growth," American Economic Review, American Economic Association, vol. 85(5), pages 1138-51, December.
  20. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-62, April.
  21. Bachmann, Rüdiger & Bayer, Christian, 2013. "‘Wait-and-See’ business cycles?," Journal of Monetary Economics, Elsevier, vol. 60(6), pages 704-719.
  22. Catherine Doz & Domenico Giannone & Lucrezia Reichlin, 2008. "A Quasi Maximum Likelihood Approach for Large Approximate Dynamic Factor Models," Working Papers ECARES 2008_034, ULB -- Universite Libre de Bruxelles.
  23. James H. Stock & Mark W. Watson, 2005. "Implications of Dynamic Factor Models for VAR Analysis," NBER Working Papers 11467, National Bureau of Economic Research, Inc.
  24. M. Ayhan Kose & Christopher Otrok & Eswar S. Prasad, 2008. "Global Business Cycles: Convergence or Decoupling?," NBER Working Papers 14292, National Bureau of Economic Research, Inc.
  25. Eric R. Sims, 2012. "Uncertainty and Economic Activity: Evidence from Business Survey Data," Working Papers 014, University of Notre Dame, Department of Economics, revised Jun 2012.
  26. repec:pit:wpaper:321 is not listed on IDEAS
  27. Ben S. Bernanke & Jean Boivin & Piotr Eliasz, 2005. "Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach," The Quarterly Journal of Economics, Oxford University Press, vol. 120(1), pages 387-422.
  28. Ball, Laurence, 1992. "Why does high inflation raise inflation uncertainty?," Journal of Monetary Economics, Elsevier, vol. 29(3), pages 371-388, June.
  29. Baillie, Richard T & Chung, Ching-Fan & Tieslau, Margie A, 1996. "Analysing Inflation by the Fractionally Integrated ARFIMA-GARCH Model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(1), pages 23-40, Jan.-Feb..
  30. Jesús Fernández-Villaverde & Juan Rubio-Ramírez, 2010. "Macroeconomics and Volatility: Data, Models, and Estimation," NBER Working Papers 16618, National Bureau of Economic Research, Inc.
  31. Michelle Alexopoulos & Jon Cohen, 2009. "Uncertain Times, uncertain measures," Working Papers tecipa-352, University of Toronto, Department of Economics.
  32. Richard, Jean-Francois & Zhang, Wei, 2007. "Efficient high-dimensional importance sampling," Journal of Econometrics, Elsevier, vol. 141(2), pages 1385-1411, December.
  33. Seung C. Ahn & Alex R. Horenstein, 2013. "Eigenvalue Ratio Test for the Number of Factors," Econometrica, Econometric Society, vol. 81(3), pages 1203-1227, 05.
  34. DeJong, David Neil & Dharmarajan, Hariharan & Liesenfeld, Roman & Moura, Guilherme V. & Richard, Jean-François, 2009. "Efficient likelihood evaluation of state-space representations," Economics Working Papers 2009,02, Christian-Albrechts-University of Kiel, Department of Economics.
  35. Mario Forni & Lucrezia Reichlin, 1998. "Let's Get Real: A Factor Analytical Approach to Disaggregated Business Cycle Dynamics," Review of Economic Studies, Oxford University Press, vol. 65(3), pages 453-473.
  36. Mario Forni & Domenico Giannone & Marco Lippi & Lucrezia Reichlin, 2007. "Opening the Black Box: Structural Factor Models with Large Cross-Sections," Center for Economic Research (RECent) 008, University of Modena and Reggio E., Dept. of Economics "Marco Biagi".
  37. M. Ayhan Kose & Christopher Otrok & Charles H. Whiteman, 2003. "International Business Cycles: World, Region, and Country-Specific Factors," American Economic Review, American Economic Association, vol. 93(4), pages 1216-1239, September.
  38. Kilian, Lutz, 2006. "Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market," CEPR Discussion Papers 5994, C.E.P.R. Discussion Papers.
  39. Bai, Jushan & Ng, Serena, 2008. "Large Dimensional Factor Analysis," Foundations and Trends(R) in Econometrics, now publishers, vol. 3(2), pages 89-163, June.
  40. Massimiliano Marcellino & Christian Schumacher, 2010. "Factor MIDAS for Nowcasting and Forecasting with Ragged-Edge Data: A Model Comparison for German GDP," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 72(4), pages 518-550, 08.
  41. Alexei Onatski, 2010. "Determining the Number of Factors from Empirical Distribution of Eigenvalues," The Review of Economics and Statistics, MIT Press, vol. 92(4), pages 1004-1016, November.
  42. Giannone, Domenico & Reichlin, Lucrezia & Sala, Luca, 2002. "Tracking Greenspan: Systematic and Unsystematic Monetary Policy Revisited," CEPR Discussion Papers 3550, C.E.P.R. Discussion Papers.
  43. Bai, Jushan & Ng, Serena, 2007. "Determining the Number of Primitive Shocks in Factor Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 52-60, January.
  44. Rüdiger Bachmann & Christian Bayer, 2011. "Uncertainty Business Cycles - Really?," NBER Working Papers 16862, National Bureau of Economic Research, Inc.
Full references (including those not matched with items on IDEAS)

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

When requesting a correction, please mention this item's handle: RePEc:ces:ifowps:_167. See general information about how to correct material in RePEc.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Klaus Wohlrabe)

If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

If references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

Please note that corrections may take a couple of weeks to filter through the various RePEc services.

This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.