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What Happens After A Technology Shock? A Bayesian Perspective

  • Ossama Mikhail

    (University of Central Florida)

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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File URL: http://128.118.178.162/eps/mac/papers/0510/0510016.pdf
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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
Contact details of provider: Web page: http://128.118.178.162

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  1. 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.
  2. Lawrence J. Christiano & Martin Eichenbaum & Charles Evans, 1994. "The effects of monetary policy shocks: evidence from the flow of funds," Proceedings, Federal Reserve Bank of Dallas, issue Apr.
  3. Kadiyala, K Rao & Karlsson, Sune, 1997. "Numerical Methods for Estimation and Inference in Bayesian VAR-Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(2), pages 99-132, March-Apr.
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  7. 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.
  8. Miles S. Kimball & John G. Fernald & Susanto Basu, 2006. "Are Technology Improvements Contractionary?," American Economic Review, American Economic Association, vol. 96(5), pages 1418-1448, December.
  9. Gali, J., 1996. "Technology, Employment, and the Business Cycle: Do Technology Shocks Explain Aggregate Fluctuations?," Working Papers 96-28, C.V. Starr Center for Applied Economics, New York University.
  10. V. V. Chari & Patrick J. Kehoe & Ellen R. McGrattan, 2005. "A critique of structural VARs using real business cycle theory," Working Papers 631, Federal Reserve Bank of Minneapolis.
  11. Gali, Jordi, 1992. "How Well Does the IS-LM Model Fit Postwar U.S. Data," The Quarterly Journal of Economics, MIT Press, vol. 107(2), pages 709-38, May.
  12. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
  13. Lawrence J. Christiano & Martin Eichenbaum & Robert J. Vigfusson, 2003. "The response of hours to a technology shock: evidence based on direct measures of technology," International Finance Discussion Papers 790, Board of Governors of the Federal Reserve System (U.S.).
  14. Neville Francis & Michael T. Owyang & Athena T. Theodorou, 2003. "The use of long-run restrictions for the identification of technology shocks," Review, Federal Reserve Bank of St. Louis, issue Nov, pages 53-66.
  15. Zheng Liu & Louis Phaneuf, 2004. "What Explains the Effects of Technology Shocks on Labor Market Dynamics?," Emory Economics 0414, Department of Economics, Emory University (Atlanta).
  16. Dorfman, Jeffrey H., 1995. "A numerical bayesian test for cointegration of AR processes," Journal of Econometrics, Elsevier, vol. 66(1-2), pages 289-324.
  17. Koop, Gary, 1991. "Cointegration tests in present value relationships : A Bayesian look at the bivariate properties of stock prices and dividends," Journal of Econometrics, Elsevier, vol. 49(1-2), pages 105-139.
  18. Lawrence J. Christiano & Martin Eichenbaum & Robert Vigfusson, 2003. "What happens after a technology shock?," International Finance Discussion Papers 768, Board of Governors of the Federal Reserve System (U.S.).
  19. Bauwens, L. & Lubrano, M., . "Identification restrictions and posterior densities in cointegrated Gaussian VAR system," CORE Discussion Papers RP -1206, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  20. Jordi Galí & Pau Rabanal, 2004. "Technology Shocks and Aggregate Fluctuations; How Well Does the RBC Model Fit Postwar U.S. Data?," IMF Working Papers 04/234, International Monetary Fund.
  21. Galí, Jordi, 2005. "Trends in Hours, Balanced Growth and the Role of Technology in the Business Cycle," CEPR Discussion Papers 4915, C.E.P.R. Discussion Papers.
  22. Shawn Ni & Dongchu Sun, 2005. "Bayesian Estimates for Vector Autoregressive Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 105-117, January.
  23. 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.
  24. Lucas, Robert Jr, 1976. "Econometric policy evaluation: A critique," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 1(1), pages 19-46, January.
  25. Bauwens, Luc & Lubrano, Michel & Richard, Jean-Francois, 2000. "Bayesian Inference in Dynamic Econometric Models," OUP Catalogue, Oxford University Press, number 9780198773139, March.
  26. John Shea, 1999. "What Do Technology Shocks Do?," NBER Chapters, in: NBER Macroeconomics Annual 1998, volume 13, pages 275-322 National Bureau of Economic Research, Inc.
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