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Identifying News Shocks with Forecast Data

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  • Yasuo Hirose
  • Takushi Kurozumi

Abstract

The empirical importance of news shocks?anticipated future shocks?in business cycle fluctuations has been explored by using only actual data when estimating models augmented with news shocks. This paper additionally exploits forecast data to identify news shocks in a canonical dynamic stochastic general equilibrium model. The estimated model shows new empirical evidence that technology news shocks are a major source of fluctuations in U.S. output growth. Exploiting the forecast data not only generates more precise estimates of news shocks and other parameters in the model, but also increases the contribution of technology news shocks to the fluctuations.

Suggested Citation

  • Yasuo Hirose & Takushi Kurozumi, 2019. "Identifying News Shocks with Forecast Data," Globalization Institute Working Papers 366, Federal Reserve Bank of Dallas.
    • Handle: RePEc:fip:feddgw:366
    DOI: 10.24149/gwp366
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    Cited by:

    1. Iskrev, Nikolay, 2018. "Are asset price data informative about news shocks? A DSGE perspective," Working Paper Series 2161, European Central Bank.
    2. Sohei Kaihatsu & Takushi Kurozumi, 2014. "Sources of Business Fluctuations: Financial or Technology Shocks?," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 17(2), pages 224-242, April.
    3. Iskrev, Nikolay, 2019. "On the sources of information about latent variables in DSGE models," European Economic Review, Elsevier, vol. 119(C), pages 318-332.
    4. Bartosz Maćkowiak & Mirko Wiederholt, 2021. "Rational Inattention and the Business Cycle Effects of Productivity and News Shocks," SciencePo Working papers Main hal-03878704, HAL.
    5. MATSUMAE Tatsuyoshi & HASUMI Ryo, 2016. "Impacts of Government Spending on Unemployment: Evidence from a Medium-scale DSGE Model(in Japanese)," ESRI Discussion paper series 329, Economic and Social Research Institute (ESRI).
    6. Miyamoto, Wataru & Nguyen, Thuy Lan, 2020. "The expectational effects of news in business cycles: Evidence from forecast data," Journal of Monetary Economics, Elsevier, vol. 116(C), pages 184-200.
    7. Milani, Fabio, 2017. "Sentiment and the U.S. business cycle," Journal of Economic Dynamics and Control, Elsevier, vol. 82(C), pages 289-311.
    8. Jonathan J. Adams & Mr. Philip Barrett, 2023. "Identifying News Shocks from Forecasts," IMF Working Papers 2023/208, International Monetary Fund.
    9. Milani, Fabio & Rajbhandari, Ashish, 2020. "Observed expectations, news shocks, and the business cycle," Research in Economics, Elsevier, vol. 74(2), pages 95-118.
    10. Jinill Kim & Seth Pruitt, 2017. "Estimating Monetary Policy Rules When Nominal Interest Rates Are Stuck at Zero," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 49(4), pages 585-602, June.
    11. Dongho Song & Jenny Tang, 2023. "News-Driven Uncertainty Fluctuations," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 41(3), pages 968-982, July.
    12. Cole, Stephen J., 2020. "The influence of learning and price-level targeting on central bank forward guidance," Journal of Macroeconomics, Elsevier, vol. 65(C).
    13. Thuy Lan Nguyen & Wataru Miyamoto, 2014. "News shocks and Business cycles: Evidence from forecast data," 2014 Meeting Papers 259, Society for Economic Dynamics.

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    More about this item

    Keywords

    business cycle fluctuations; Technology Shock; Technology News Shock; Forecast Data; Bayesian Estimation;
    All these keywords.

    JEL classification:

    • E30 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - General (includes Measurement and Data)
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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