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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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Cited by:
  1. Yongsung Chang & Sun-Bin Kim & Frank Schorfheide, 2010. "Labor-Market Heterogeneity, Aggregation, and the Lucas Critique," NBER Working Papers 16401, National Bureau of Economic Research, Inc.
  2. Benjamin Born & Johannes Pfeifer, 2013. "Policy Risk and the Business Cycle," CESifo Working Paper Series 4336, CESifo Group Munich.
  3. S. Boragan Aruoba & Luigi Bocola & Frank Schorfheide, 2013. "Assessing DSGE model nonlinearities," Working Papers 13-47, Federal Reserve Bank of Philadelphia.
  4. Kociecki, Andrzej, 2013. "Bayesian Approach and Identification," MPRA Paper 46538, University Library of Munich, Germany.

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