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Are asset price data informative about news shocks? A DSGE perspective

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  • Nikolay Iskrev

Abstract

Standard economic intuition suggests that asset prices are more sensitive to news than other economic aggregates.This has led many researchers to conclude that asset price data would be very useful for the estimation of business cycle models containing news shocks.This paper shows how to formally evaluate the information content of observed variables with respect to unobservedshocks in structural macroeconomic models.The proposed methodology is applied to two different real business cycle models with news shocks.The contribution of asset prices is found to be relatively small.The methodology is general and can be used to measure the informational importance of observables with respect to latent variables in DSGE models.Thus,it provides a framework for systematic treatment of such issues,which are usually discussed in an informal manner in the literature.

Suggested Citation

  • Nikolay Iskrev, 2018. "Are asset price data informative about news shocks? A DSGE perspective," Working Papers REM 2018/33, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
  • Handle: RePEc:ise:remwps:wp0332018
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    References listed on IDEAS

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    1. Domenico Giannone & Lucrezia Reichlin, 2006. "Does information help recovering structural shocks from past observations?," Journal of the European Economic Association, MIT Press, vol. 4(2-3), pages 455-465, 04-05.
    2. Andr? Kurmann & Christopher Otrok, 2013. "News Shocks and the Slope of the Term Structure of Interest Rates," American Economic Review, American Economic Association, vol. 103(6), pages 2612-2632, October.
    3. Stefan Avdjiev, 2016. "News Driven Business Cycles and Data on Asset Prices in Estimated DSGE Models," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 20, pages 181-197, April.
    4. Xiao-Li Meng & Xianchao Xie, 2014. "I Got More Data, My Model is More Refined, but My Estimator is Getting Worse! Am I Just Dumb?," Econometric Reviews, Taylor & Francis Journals, vol. 33(1-4), pages 218-250, June.
    5. Beaudry, Paul & Portier, Franck, 2004. "An exploration into Pigou's theory of cycles," Journal of Monetary Economics, Elsevier, vol. 51(6), pages 1183-1216, September.
    6. Thuy Lan Nguyen & Wataru Miyamoto, 2014. "News shocks and Business cycles: Evidence from forecast data," 2014 Meeting Papers 259, Society for Economic Dynamics.
    7. Fabio Milani & Ashish Rajrhandari, 2012. "Observed Expectations, News Shocks, and the Business Cycle," Working Papers 121305, University of California-Irvine, Department of Economics.
    8. 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.
    9. Soccorsi, Stefano, 2016. "Measuring nonfundamentalness for structural VARs," Journal of Economic Dynamics and Control, Elsevier, vol. 71(C), pages 86-101.
    10. Massimo Franchi & Paolo Paruolo, 2015. "Minimality of State Space Solutions of DSGE Models and Existence Conditions for Their VAR Representation," Computational Economics, Springer;Society for Computational Economics, vol. 46(4), pages 613-626, December.
    11. Paul Beaudry & Patrick Fève & Alain Guay & Franck Portier, 2015. "When is Nonfundamentalness in VARs a Real Problem? An Application to News Shocks," NBER Working Papers 21466, National Bureau of Economic Research, Inc.
    12. Nikolay, Iskrev, 2014. "Choosing the variables to estimate singular DSGE models: Comment," Dynare Working Papers 41, CEPREMAP.
    13. Franchi, Massimo & Vidotto, Anna, 2013. "A check for finite order VAR representations of DSGE models," Economics Letters, Elsevier, vol. 120(1), pages 100-103.
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    Cited by:

    1. Faccini, Renato & Melosi, Leonardo, 2018. "Pigouvian Cycles," CEPR Discussion Papers 13370, C.E.P.R. Discussion Papers.

    More about this item

    Keywords

    DSGE models; News Shocks; Asset prices; Information; Identification;

    JEL classification:

    • 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
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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