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Methods versus substance: measuring the effects of technology shocks on hours

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  • José-Víctor Ríos-Rull
  • Frank Schorfheide
  • Cristina Fuentes-Albero
  • Raul Santaeulalia-Llopis
  • Maxym Kryshko

Abstract

In this paper, we employ both calibration and modern (Bayesian) estimation methods to assess the role of neutral and investment-specific technology shocks in generating fluctuations in hours. Using a neoclassical stochastic growth model, we show how answers are shaped by the identification strategies and not by the statistical approaches. The crucial parameter is the labor supply elasticity. Both a calibration procedure that uses modern assessments of the Frisch elasticity and the estimation procedures result in technology shocks accounting for 2% to 9% of the variation in hours worked in the data. We infer that we should be talking more about identification and less about the choice of particular quantitative approaches.

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Bibliographic Info

Paper provided by Federal Reserve Bank of Minneapolis in its series Staff Report with number 433.

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Date of creation: 2009
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Handle: RePEc:fip:fedmsr:433

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Keywords: Business cycles ; Technology - Economic aspects;

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Blog mentions

As found by EconAcademics.org, the blog aggregator for Economics research:
  1. Technology shocks and hours: it is the identification, stupid!
    by Economic Logician in Economic Logic on 2009-10-13 14:05:00

RePEc Biblio mentions

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  1. > Macroeconomics > Monetary Theory
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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. Jesús Rodríguez López, 2010. "Growth, fluctuations and technology in the U.S. post-war economy," Working Papers 10.01, Universidad Pablo de Olavide, Department of Economics.
  3. Enrique Martínez-García & Diego Vilán & Mark Wynne, 2012. "Bayesian estimation of NOEM models: identification and inference in small samples," Globalization and Monetary Policy Institute Working Paper 105, Federal Reserve Bank of Dallas.
  4. Frank Schorfheide, 2012. "EconomicDynamics Interviews Frank Schorfheide on DSGE Model Estimation," EconomicDynamics Newsletter, Review of Economic Dynamics, vol. 13(2), April.
  5. Justiniano, Alejandro & Preston, Bruce, 2010. "Can structural small open-economy models account for the influence of foreign disturbances?," Journal of International Economics, Elsevier, vol. 81(1), pages 61-74, May.
  6. Nikolay Iskrev, 2013. "On the distribution of information in the moment structure of DSGE models," 2013 Meeting Papers 339, Society for Economic Dynamics.
  7. Cristiano Cantore & Filippo Ferroni & Miguel A León-Ledesma, 2012. "Interpreting the Hours-Technology time-varying relationship," Studies in Economics 1201, Department of Economics, University of Kent.
  8. Fang Yao, 2010. "Aggregate Hazard Function in Price-Setting: A Bayesian Analysis Using Macro Data," SFB 649 Discussion Papers SFB649DP2010-020, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  9. Frank Schorfheide, 2011. "Estimation and Evaluation of DSGE Models: Progress and Challenges," NBER Working Papers 16781, National Bureau of Economic Research, Inc.
  10. Franke, Reiner & Jang, Tae-Seok & Sacht, Stephen, 2012. "Moment matching versus Bayesian estimation: Backward-looking behaviour in a New-Keynesian baseline model," Economics Working Papers 2012-08, Christian-Albrechts-University of Kiel, Department of Economics.
  11. Alejandro Justiniano & Claudio Michelacci, 2011. "The Cyclical Behavior of Equilibrium Unemployment and Vacancies in the United States and Europe," NBER Chapters, in: NBER International Seminar on Macroeconomics 2011, pages 169-235 National Bureau of Economic Research, Inc.
  12. Alejandro Justiniano & Claudio Michelacci, 2011. "The Cyclical Behavior of Equilibrium Unemployment and Vacancies in the US and Europe," NBER Working Papers 17429, National Bureau of Economic Research, Inc.

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