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What Happens After A Technology Shock? A Bayesian Perspective

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  • Ossama Mikhail

    (University of Central Florida)

Abstract

This paper investigates the effect of a positive technology shock on per capita hours worked within the class of Bayesian Vector Auto-Regressive [BVAR] models. Such a framework avoids the current debate regarding the specification issue of per capita hours [level versus first-difference stationary]. Six priors are considered and for each, we examine the impulse responses of per capita hours following a positive technology shock. The marginal posteriors of the VAR parameters are generated using the Markov Chain Monte Carlo (MCMC) Gibbs sampler. We find that the estimation of the VAR yields significantly different estimates under competing priors. Using the Francis and Ramey (2004, UCSD working paper) new measure for per capita hours, and after imposing the identifying restrictions (i.e., Blanchard-Quah and sign restrictions), the results show that per capita hours worked rise following a positive technology shock - if one [objectively] assumes a non-informative prior.

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Bibliographic Info

Paper provided by EconWPA in its series Macroeconomics with number 0510016.

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Length: 34 pages
Date of creation: 18 Oct 2005
Date of revision:
Handle: RePEc:wpa:wuwpma:0510016

Note: Type of Document - pdf; pages: 34
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Web page: http://128.118.178.162

Related research

Keywords: Bayesian Vector Auto-Regression (BVAR); Blanchard-Quah Identification; Markov Chain Monte Carlo (MCMC) Gibbs Sampler; Technology Shock; Real Business Cycle (RBC);

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References

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  3. Neville Francis & Valerie A. Ramey, 2002. "Is the Technology-Driven Real Business Cycle Hypothesis Dead?," NBER Working Papers 8726, National Bureau of Economic Research, Inc.
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  17. Jordi Galí, 2005. "Trends in hours, balanced growth and the role of technology in the business cycle," Economics Working Papers 829, Department of Economics and Business, Universitat Pompeu Fabra.
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  22. Christopher A. Sims, 2005. "Commentary on "trends in hours, balanced growth, and the role of technology in the business cycle"," Review, Federal Reserve Bank of St. Louis, issue Jul, pages 487-492.
  23. Mark P. Taylor, 2004. "Estimating structural macroeconomic shocks through long-run recursive restrictions on vector autoregressive models: the problem of identification," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 9(3), pages 229-244.
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