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Measuring macroeconomic uncertainty: A cross-country analysis

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  • Dibiasi, Andreas
  • Sarferaz, Samad

Abstract

This paper constructs internationally consistent measures of macroeconomic uncertainty. Our econometric framework extracts uncertainty from revisions in data obtained from standardized national accounts. Applying our model to post-WWII real-time data, we estimate macroeconomic uncertainty for 39 countries. The cross-country dimension of our uncertainty data allows us to study the impact of uncertainty shocks under varying degrees of employment protection legislation. Our empirical findings suggest that the effects of uncertainty shocks are stronger and more persistent in countries with low employment protection compared to countries with high employment protection. These empirical findings are in line with a theoretical model under varying firing cost.

Suggested Citation

  • Dibiasi, Andreas & Sarferaz, Samad, 2023. "Measuring macroeconomic uncertainty: A cross-country analysis," European Economic Review, Elsevier, vol. 153(C).
  • Handle: RePEc:eee:eecrev:v:153:y:2023:i:c:s0014292123000120
    DOI: 10.1016/j.euroecorev.2023.104383
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    Cited by:

    1. Andreas Dibiasi & David Iselin, 2021. "Measuring Knightian uncertainty," Empirical Economics, Springer, vol. 61(4), pages 2113-2141, October.
    2. Kamalyan, Hayk, 2022. "Data revisions and the effects of monetary policy volatility," Economics Letters, Elsevier, vol. 215(C).
    3. Efrem Castelnuovo, 2022. "Uncertainty Before and During COVID-19: A Survey," "Marco Fanno" Working Papers 0279, Dipartimento di Scienze Economiche "Marco Fanno".
    4. Gnangnon, Sèna Kimm, 2023. "Effect of Economic Uncertainty on Remittances Flows from Developed Countries," EconStor Preprints 279480, ZBW - Leibniz Information Centre for Economics.
    5. Oscar Claveria, 2021. "On the Aggregation of Survey-Based Economic Uncertainty Indicators Between Different Agents and Across Variables," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 17(1), pages 1-26, April.
    6. repec:zbw:bofrdp:2021_001 is not listed on IDEAS
    7. Oscar Claveria, 2021. "Disagreement on expectations: firms versus consumers," SN Business & Economics, Springer, vol. 1(12), pages 1-23, December.
    8. Andreas Dibiasi & Heiner Mikosch & Samad Sarferaz, 2021. "Uncertainty Shocks, Adjustment Costs and Firm Beliefs: Evidence From a Representative Survey," KOF Working papers 21-496, KOF Swiss Economic Institute, ETH Zurich.
    9. Giovanni Caggiano & Efrem Castelnuovo, 2021. "Global Uncertainty," CESifo Working Paper Series 8885, CESifo.

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    More about this item

    Keywords

    Uncertainty shocks; Real-time data; Rational forecast error; Employment protection legislation; System of national accounts;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • J8 - Labor and Demographic Economics - - Labor Standards

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