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Methods versus substance: Measuring the effects of technology shocks

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  • Ríos-Rull, José-Víctor
  • Schorfheide, Frank
  • Fuentes-Albero, Cristina
  • Kryshko, Maxym
  • Santaeulàlia-Llopis, Raül

Abstract

Calibration and modern (Bayesian) estimation methods for a neoclassical stochastic growth model are applied to make the case that the identification of key parameters, rather than quantitative methodologies per se, is responsible for empirical findings. For concreteness, the model is used to measure the contributions of technology shocks to the business cycle fluctuations of hours worked and output. Along the way, new insights are provided in the parameter identification associated with likelihood-based estimation, the sensitivity of likelihood-based estimation to the choice of structural shocks is assessed, and Bayesian model averaging is used to aggregate findings obtained from different DSGE model specifications.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Monetary Economics.

Volume (Year): 59 (2012)
Issue (Month): 8 ()
Pages: 826-846

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Handle: RePEc:eee:moneco:v:59:y:2012:i:8:p:826-846

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Web page: http://www.elsevier.com/locate/inca/505566

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References

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Cited by:
  1. S. Borağan Aruoba & Luigi Bocola & Frank Schorfheide, 2013. "Assessing DSGE Model Nonlinearities," NBER Working Papers 19693, National Bureau of Economic Research, Inc.
  2. Yongsung Chang & Sun-Bin Kim & Frank Schorfheide, 2010. "Labor-Market Heterogeneity, Aggregation, and the Lucas Critique," RCER Working Papers 556, University of Rochester - Center for Economic Research (RCER).
  3. Kociecki, Andrzej, 2013. "Bayesian Approach and Identification," MPRA Paper 46538, University Library of Munich, Germany.
  4. Benjamin Born & Johannes Pfeifer, 2011. "Policy Risk and the Business Cycle," Bonn Econ Discussion Papers bgse06_2011, University of Bonn, Germany.

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