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Identifying News Shocks with Forecast Data

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  • Yasuo Hirose
  • Takushi Kurozumi

Abstract

Recent studies attempt to quantify the empirical importance of news shocks (i.e., anticipated future shocks) in business cycle fluctuations. This paper identifies news shocks in a dynamic stochastic general equilibrium model estimate with not only actual data but also forecast data. The estimation results show new empirical evidence that anticipated future technology shocks are the most important driving force of U.S. business cycles. The use of the forecast data makes the anticipated shocks play a much more important role in fitting model-implied expectations to this data, since such shocks have persistent effects on the expectations and thereby help to replicate the observed persistence of the forecasts.

Suggested Citation

  • Yasuo Hirose & Takushi Kurozumi, 2012. "Identifying News Shocks with Forecast Data," CAMA Working Papers 2012-01, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
  • Handle: RePEc:een:camaaa:2012-01
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    Cited by:

    1. 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.
    2. Nikolay Iskrev, 2018. "Are asset price data informative about news shocks? A DSGE perspective," Working Papers w201802, Banco de Portugal, Economics and Research Department.
    3. Cole, Stephen J., 2020. "The influence of learning and price-level targeting on central bank forward guidance," Journal of Macroeconomics, Elsevier, vol. 65(C).
    4. Milani, Fabio, 2017. "Sentiment and the U.S. business cycle," Journal of Economic Dynamics and Control, Elsevier, vol. 82(C), pages 289-311.
    5. Thuy Lan Nguyen & Wataru Miyamoto, 2014. "News shocks and Business cycles: Evidence from forecast data," 2014 Meeting Papers 259, Society for Economic Dynamics.
    6. Milani, Fabio & Rajbhandari, Ashish, 2020. "Observed expectations, news shocks, and the business cycle," Research in Economics, Elsevier, vol. 74(2), pages 95-118.
    7. 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.
    8. 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).
    9. Dongho Song & Jenny Tang, 2018. "News-driven uncertainty fluctuations," Working Papers 18-3, Federal Reserve Bank of Boston.
    10. 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.
    11. Iskrev, Nikolay, 2019. "On the sources of information about latent variables in DSGE models," European Economic Review, Elsevier, vol. 119(C), pages 318-332.

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    More about this item

    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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