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Uncertainty and the macroeconomy: evidence from an uncertainty composite indicator

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  • Amélie Charles
  • Olivier Darné
  • Fabien Tripier

Abstract

This article proposes a uncertainty composite indicator (UCI) based on three distinct sources of uncertainty (namely financial, political, and macroeconomic) for the US economy on the period 1985–2015. For that, we use a dynamic factor model, summarizing efficiently six individual uncertainty proxies, namely two macroeconomic and financial uncertainty factors based on the unpredictability, a measure of (micro)economic uncertainty, the implied volatility index, the corporate bond spreads, and an index of economic policy uncertainty. We then compare the effects of uncertainty on economic activity when the UCI is used instead of individual uncertainty proxies in structural VAR models. The interest of our UCI is to synthesize theses effects within one measure of uncertainty. Overall, the UCI was able to account for the most important dynamics of uncertainty which play an important role in business cycles.

Suggested Citation

  • Amélie Charles & Olivier Darné & Fabien Tripier, 2018. "Uncertainty and the macroeconomy: evidence from an uncertainty composite indicator," Applied Economics, Taylor & Francis Journals, vol. 50(10), pages 1093-1107, February.
  • Handle: RePEc:taf:applec:v:50:y:2018:i:10:p:1093-1107
    DOI: 10.1080/00036846.2017.1349294
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    Cited by:

    1. Nikolay Hristov & Markus Roth, 2019. "Uncertainty Shocks and Financial Crisis Indicators," CESifo Working Paper Series 7839, CESifo.
    2. Bouoiyour, Jamal & Selmi, Refk & Wohar, Mark E., 2018. "Measuring the response of gold prices to uncertainty: An analysis beyond the mean," Economic Modelling, Elsevier, vol. 75(C), pages 105-116.
    3. Zied Ftiti & Fredj Jawadi, 2019. "Forecasting Inflation Uncertainty in the United States and Euro Area," Computational Economics, Springer;Society for Computational Economics, vol. 54(1), pages 455-476, June.
    4. Mei-Chih Wang & Pao-Lan Kuo & Chan-Sheng Chen & Chien-Liang Chiu & Tsangyao Chang, 2020. "Yield Spread and Economic Policy Uncertainty: Evidence from Japan," Sustainability, MDPI, Open Access Journal, vol. 12(10), pages 1-14, May.
    5. Oscar Claveria, 2020. "“Measuring and assessing economic uncertainty”," AQR Working Papers 2012003, University of Barcelona, Regional Quantitative Analysis Group, revised Jul 2020.
    6. Yingting Yi & Jiangshui Luo & Michael Wübbenhorst, 2020. "Research on political instability, uncertainty and risk during 1953–2019: a scientometric review," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(2), pages 1051-1076, May.
    7. Bartsch, Zachary, 2019. "Economic policy uncertainty and dollar-pound exchange rate return volatility," Journal of International Money and Finance, Elsevier, vol. 98(C), pages 1-1.
    8. Ömer YALÇINKAYA & Muhammet DAŞTAN, 2020. "Effects of Global Economic, Political and Geopolitical Uncertainties on the Turkish Economy: A SVAR Analysis," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 97-116, March.
    9. Oscar Claveria, 0. "Uncertainty indicators based on expectations of business and consumer surveys," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 0, pages 1-23.

    More about this item

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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