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IDENTIFYING NEWS SHOCKS WITH FORECAST DATA

Published online by Cambridge University Press:  10 September 2019

Yasuo Hirose  and
Takushi Kurozumi
Yasuo Hirose*
Affiliation:
Keio University
Takushi Kurozumi
Affiliation:
Bank of Japan
*
Address correspondence to: Yasuo Hirose, Faculty of Economics, Keio University, 2-15-45 Mita, Minato-ku, Tokyo 108-8345, Japan. e-mail: [email protected].
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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 US 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.

Keywords

Business Cycle FluctuationTechnology ShockTechnology News ShockForecast DataBayesian Estimation
Type
Articles
Information
Macroeconomic Dynamics , Volume 25 , Issue 6 , September 2021 , pp. 1442 - 1471
Copyright
© Cambridge University Press 2019

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Footnotes

The authors are grateful for comments and discussions to Klaus Adam, Kosuke Aoki, William Barnett (the editor), Paul Beaudry, Francesco Bianchi, Ryan Chahrour, Hess Chung, Nicolas Crouzet, Richard Dennis, Taeyoung Doh, Ippei Fujiwara, Christopher Gust, Craig Hakkio, William Hawkins, Timo Henckel, Hirokazu Ishise, Jan Jacobs, Jinill Kim, Edward Knotek, Andre Kurmann, Kevin Lansing, Thomas Lubik, Fabio Milani, Toshihiko Mukoyama, Masao Ogaki, Jordan Rappaport, Hiroatsu Tanaka, Kozo Ueda, Shaun Vahey, Willem Van Zandweghe, Robert Vigfusson, Todd Walker, John Williams, Tomoaki Yamada, colleagues at the Bank of Japan, an anonymous associate editor, and two anonymous referees, as well as participants at International Conference on Computing in Economics and Finance, Annual Conference of the Royal Economic Society, Conference on Expectations in Dynamic Macroeconomic Models hosted by the Federal Reserve Bank of San Francisco, Dynare Conference, Hitotsubashi University International Conference on Frontiers in Macroeconometrics, and seminars at Australian National University, Hosei University, Keio University, Kobe University, Meiji University, the Federal Reserve Board, the Federal Reserve Bank of Kansas City, and the Bank of Japan. The views expressed herein are those of the authors and do not necessarily reflect the official views of the Bank of Japan.

References

REFERENCES

Barsky, R. B. and Sims, E. R. (2011) News shocks and business cycles. Journal of Monetary Economics 58(3), 273–289.CrossRefGoogle Scholar
Barsky, R. B. and Sims, E. R. (2012) Information, animal spirits, and the meaning of innovations in consumer confidence. American Economic Review 102(4), 1343–1377.CrossRefGoogle Scholar
Beaudry, P. and Portier, F. (2004) An exploration into Pigou’s theory of cycles. Journal of Monetary Economics 51(6), 1183–1216.CrossRefGoogle Scholar
Beaudry, P. and Portier, F. (2006) Stock prices, news, and economic fluctuations. American Economic Review 96(4), 1293–1307.CrossRefGoogle Scholar
Beaudry, P. and Portier, F. (2014) News-driven business cycles: Insights and challenges. Journal of Economic Literature 52(4), 993–1074.CrossRefGoogle Scholar
Blanchard, O. J., L’Huillier, J.-P and Lorenzoni, G. (2013) News, noise, and fluctuations: An empirical exploration. American Economic Review 103(7), 3045–3070.CrossRefGoogle Scholar
Born, B., Peter, A. and Pfeifer, J. (2013) Fiscal news and macroeconomic volatility. Journal of Economic Dynamics and Control 37, 2582–2601.CrossRefGoogle Scholar
Brooks, S. P. and Gelman, A. (1998) General methods for monitoring convergence of iterative simulations. Journal of Computational and Graphical Statistics 7(4), 434–455.Google Scholar
Calvo, G. A. (1983) Staggered prices in a utility-maximizing framework. Journal of Monetary Economics 12(3), 383–398.CrossRefGoogle Scholar
Chahrour, R. and Jurado, K. (2018) News or noise? The missing link. American Economic Review 108(7), 1702–1736.CrossRefGoogle Scholar
Christiano, L. J., Eichenbaum, M. and Evans, C. L. (2005) Nominal rigidities and the dynamic effects of a shock to monetary policy. Journal of Political Economy 113(1), 1–45.CrossRefGoogle Scholar
Christiano, L., Ilut, C. L., Motto, R. and Rostagno, M. (2010) Monetary Policy and Stock Market Booms. National Bureau of Economic Research Working Paper No. 16402.CrossRefGoogle Scholar
Clarida, R., Galí, J. and Gertler, M. (2000) Monetary policy rules and macroeconomic stability: Evidence and some theory. Quarterly Journal of Economics 115(1), 147–180.CrossRefGoogle Scholar
Coibion, O., Gorodnichenko, Y. and Kamdar, R. (2018) The formation of expectations, inflation and the Phillips curve. Journal of Economic Literature 56(4), 1447–1491.CrossRefGoogle Scholar
Del Negro, M. and Eusepi, S. (2011) Fitting observed inflation expectations. Journal of Economic Dynamics and Control 35(12), 2105–2131.CrossRefGoogle Scholar
Fernández-Villaverde, J., Rubio-Ramírez, J. F., Sargent, T. J. and Watson, M. W. (2007) ABCs (and Ds) of understanding VARs. American Economic Review 97(3), 1021–1026.CrossRefGoogle Scholar
Fuhrer, J. C. (2017) Expectations as a source of macroeconomic persistence: Evidence from survey expectations in a dynamic macro model. Journal of Monetary Economics 86, 22–35.CrossRefGoogle Scholar
Fujiwara, I. (2010) A note on growth expectation. Macroeconomic Dynamics 14(2), 242–256.CrossRefGoogle Scholar
Fujiwara, I., Hirose, Y. and Shintani, M. (2011) Can news be a major source of aggregate fluctuations? A Bayesian DSGE approach. Journal of Money, Credit and Banking 43(1), 1–29.CrossRefGoogle Scholar
Hirose, Y. and Kurozumi, T. (2016) Changes in the Federal Reserve communication strategy: A structural investigation. Journal of Money, Credit and Banking 49(1), 171–185.CrossRefGoogle Scholar
Iskrev, N. (2010a) Local identification in DSGE models. Journal of Monetary Economics 57(2), 189–202.CrossRefGoogle Scholar
Iskrev, N. (2010b) Evaluating the Strength of Identification in DSGE Models. An a priori Approach. Bank de Portugal Economics and Research Department Working Paper No. 32/2010Google Scholar
Jaimovich, N. and Rebelo, S. T. (2009) Can news about the future drive the business cycle? American Economic Review 99(4), 1097–1118.CrossRefGoogle Scholar
Jeffreys, H. (1961) The Theory of Probability. Oxford, UK: Oxford University Press.Google Scholar
Khan, H. and Tsoukalas, J. (2012) The quantitative importance of news shocks in estimated DSGE models. Journal of Money, Credit and Banking 44(8), 1535–1561.CrossRefGoogle Scholar
King, R. G. and Rebelo, S. T. (1999) Resuscitating real business cycles. In: Taylor, J. B. and Woodford, M (eds.), Handbook of Macroeconomics, Vol. 1B, pp. 927–1007. Amsterdam, North-Holland: Elsevier Science.CrossRefGoogle Scholar
Leduc, S. and Sill, K. (2013) Expectations and economic fluctuations: An analysis using survey data. Review of Economics and Statistics 95(4), 1352–1367.CrossRefGoogle Scholar
Leeper, E. M., Walker, T. B. and Yang, S.-C. S. (2013) Fiscal foresight and information flows. Econometrica 81(3), 1115–1145.Google Scholar
Lorenzoni, G. (2009) A theory of demand shocks. American Economic Review 99(5), 2050–2084.CrossRefGoogle Scholar
Lubik, T. A. and Schorfheide, F. (2004) Testing for indeterminacy: An application to U.S. monetary policy. American Economic Review 94(1), 190–217.CrossRefGoogle Scholar
Milani, F. (2007) Expectations, learning and macroeconomic persistence. Journal of Monetary Economics 54(7), 2065–2082.CrossRefGoogle Scholar
Milani, F. (2011) Expectation shocks and learning as drivers of the business cycle. Economic Journal 121(552), 379–401.CrossRefGoogle Scholar
Milani, F. (2017) Sentiment and the U.S. business cycle. Journal of Economic Dynamics and Control 82, 289–311.CrossRefGoogle Scholar
Milani, F. and Rajbhandari, A. (2012) Observed Expectations, News Shocks, and the Business Cycle. University of California-Irvine Department of Economics Working Paper No. 121305.Google Scholar
Milani, F. and Treadwell, J. (2012) The effects of monetary policy “news” and “surprises.” Journal of Money, Credit and Banking 44(8), 1667–1692.CrossRefGoogle Scholar
Miyamoto, W. and Nguyen, T. L. (2018) The Expectational Effects of News in Business Cycles: Evidence from Forecast Data. Working Paper.Google Scholar
Ormeño, A. and Molnár, K. (2015) Using survey data of inflation expectations in the estimation of learning rational expectations models. Journal of Money, Credit and Banking 47(4), 673–699.CrossRefGoogle Scholar
Prescott, E. C. (1986) Response to a skeptic. Federal Reserve Bank of Minneapolis Quarterly Review 10(4), 28–30.Google Scholar
Schmitt-Grohé, S. and Uribe, M. (2012) What’s news in business cycles. Econometrica 80(6), 2733–2764.Google Scholar
Sims, E. (2016) What’s news in news? A cautionary note on using a variance decomposition to assess the quantitative importance of news shocks. Journal of Economic Dynamics and Control 73, 41–60.CrossRefGoogle Scholar
Slobodyan, S. and Wouters, R. (2012a) Learning in an estimated medium-scale DSGE model. Journal of Economic Dynamics and Control 36(1), 26–46.CrossRefGoogle Scholar
Slobodyan, S. and Wouters, R. (2012b) Learning in a medium-scale DSGE model with expectations based on small forecasting models. American Economic Journal: Macroeconomics 4(2), 65–101.Google Scholar
Slobodyan, S. and Wouters, R. (2017) Adaptive learning and survey expectations of inflation. Mimeo.Google Scholar
Smets, F. and Wouters, R. (2007) Shocks and frictions in US business cycles: A Bayesian DSGE approach. American Economic Review 97(3), 586–606.CrossRefGoogle Scholar
Taylor, J. B. (1993) Discretion versus policy rules in practice. Carnegie-Rochester Conference Series on Public Policy 39(1), 195–214.CrossRefGoogle Scholar
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