[go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/p/bge/wpaper/920.html
   My bibliography  Save this paper

Understanding the Sources of Macroeconomic Uncertainty

Author

Listed:
  • Barbara Rossi
  • Tatevik Sekhposyan
  • Matthieu Soupre
Abstract
We propose a decomposition to distinguish between Knightian uncertainty (ambiguity) and risk, where the first measures the uncertainty about the probability distribution generating the data, while the second measures uncertainty about the odds of the outcomes when the probability distribution is known. We use the Survey of Professional Forecasters (SPF) density forecasts to quantify overall uncertainty as well as the evolution of the different components of uncertainty over time and investigate their importance for macroeconomic fluctuations. We also study the behavior and evolution of the various components of our decomposition in a model that features ambiguity and risk.

Suggested Citation

  • Barbara Rossi & Tatevik Sekhposyan & Matthieu Soupre, 2016. "Understanding the Sources of Macroeconomic Uncertainty," Working Papers 920, Barcelona School of Economics.
    • Handle: RePEc:bge:wpaper:920
    as

    Download full text from publisher

    File URL: https://www.barcelonagse.eu/sites/default/files/working_paper_pdfs/920_0.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Rossi, Barbara & Sekhposyan, Tatevik & Soupré, Mattheiu, 2016. "Understanding the Sources of Macroeconomic Uncertainty," CEPR Discussion Papers 11415, C.E.P.R. Discussion Papers.
    2. Lawrence J. Christiano & Martin Eichenbaum & Charles L. Evans, 2005. "Nominal Rigidities and the Dynamic Effects of a Shock to Monetary Policy," Journal of Political Economy, University of Chicago Press, vol. 113(1), pages 1-45, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Meinen, Philipp & Roehe, Oke, 2017. "On measuring uncertainty and its impact on investment: Cross-country evidence from the euro area," European Economic Review, Elsevier, vol. 92(C), pages 161-179.
    2. Farmer, Roger E.A. & Nicolò, Giovanni, 2018. "Keynesian economics without the Phillips curve," Journal of Economic Dynamics and Control, Elsevier, vol. 89(C), pages 137-150.
    3. Lemoine, Matthieu & Lindé, Jesper, 2016. "Fiscal consolidation under imperfect credibility," European Economic Review, Elsevier, vol. 88(C), pages 108-141.
    4. Juan José Echavarría & Andrés González, 2012. "Choques internacionales reales y financieros y su impacto sobre la economía colombiana," Revista ESPE - Ensayos sobre Política Económica, Banco de la Republica de Colombia, vol. 30(69), pages 14-66, December.
    5. Qazi Haque & Leandro M. Magnusson, 2020. "Identification robust empirical evidence on the Euler equation in open economies," Economics Discussion / Working Papers 20-01, The University of Western Australia, Department of Economics.
    6. Anne Kathrin Funk & Daniel Kaufmann, 2022. "Do Sticky Wages Matter? New Evidence from Matched Firm Survey and Register Data," Economica, London School of Economics and Political Science, vol. 89(355), pages 689-712, July.
    7. Adnan Haider Bukhari & Safdar Ullah Khan, 2008. "A Small Open Economy DSGE Model for Pakistan," The Pakistan Development Review, Pakistan Institute of Development Economics, vol. 47(4), pages 963-1008.
    8. Lin, Tsu-ting Tim, 2015. "Working capital requirement and the unemployment volatility puzzle," Journal of Macroeconomics, Elsevier, vol. 46(C), pages 201-217.
    9. Michael T. Kiley, 2007. "Is Moderate-to-High Inflation Inherently Unstable?," International Journal of Central Banking, International Journal of Central Banking, vol. 3(2), pages 173-201, June.
    10. Leon W. Berkelmans, 2008. "Imperfect information and monetary models: multiple shocks and their consequences," Finance and Economics Discussion Series 2008-58, Board of Governors of the Federal Reserve System (U.S.).
    11. Laczó, Sarolta & Rossi, Raffaele, 2020. "Time-consistent consumption taxation," Journal of Monetary Economics, Elsevier, vol. 114(C), pages 194-220.
    12. Paul De Grauwe, 2014. "Animal Spirits and Monetary Policy," World Scientific Book Chapters, in: Exchange Rates and Global Financial Policies, chapter 18, pages 473-520, World Scientific Publishing Co. Pte. Ltd..
    13. Waters, George A., 2013. "Quantity rationing of credit and the Phillips curve," Journal of Macroeconomics, Elsevier, vol. 37(C), pages 68-80.
    14. Minford, Patrick & Wickens, Michael & Xu, Yongdeng, 2016. "Comparing different data descriptors in Indirect Inference tests on DSGE models," Economics Letters, Elsevier, vol. 145(C), pages 157-161.
    15. Champagne, Julien & Kurmann, André & Stewart, Jay, 2017. "Reconciling the divergence in aggregate U.S. wage series," Labour Economics, Elsevier, vol. 49(C), pages 27-41.
    16. Mumtaz, Haroon & Theodoridis, Konstantinos, 2017. "Common and country specific economic uncertainty," Journal of International Economics, Elsevier, vol. 105(C), pages 205-216.
    17. Barbara Annicchiarico & Fabio Dio & Francesco Felici, 2015. "Fiscal Devaluation Scenarios: A Quantitative Assessment for the Italian Economy," Open Economies Review, Springer, vol. 26(4), pages 731-785, September.
    18. Patrick Fève & Julien Matheron & Jean‐Guillaume Sahuc, 2009. "Minimum Distance Estimation and Testing of DSGE Models from Structural VARs," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(6), pages 883-894, December.
    19. Denny Lie & Anirudh S. Yadav, 2017. "Time-Varying Trend Inflation and the New Keynesian Phillips Curve in Australia," The Economic Record, The Economic Society of Australia, vol. 93(300), pages 42-66, March.
    20. Jang, Tae-Seok & Sacht, Stephen, 2017. "Modeling consumer confidence and its role for expectation formation: A horse race," Economics Working Papers 2017-04, Christian-Albrechts-University of Kiel, Department of Economics.

    More about this item

    Keywords

    uncertainty; risk; ambiguity; knightian uncertainty; survey of professional forecasters; predictive densities;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bge:wpaper:920. See general information about how to correct material in RePEc.

    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 CitEc recognized a bibliographic reference but did not link an item in RePEc 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 RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Bruno Guallar (email available below). General contact details of provider: https://edirc.repec.org/data/bargses.html .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.