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Does Cointegration Matter? An Analysis in a RBC Perspective

  • Laura Bisio
  • Andrea Faccini
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    The aim of this paper is to verify if a proper SVEC representation of a standard Real Business Cycle model exists even when the capital stock series is omitted. The argument is relevant as the common unavailability of su¢ ciently long medium-frequency capital series prevent researchers from including capital in the widespread structural VAR (SVAR) representations of DSGE models - which is supposed to be the cause of the SVAR biased estimates. Indeed, a large debate about the truncation and small sample bias a¤ecting the SVAR performance in approximating DSGE models has been recently rising. In our view, it might be the case of a smaller degree of estimates distorsions when the RBC dynamics is approximated through a SVEC model as the information provided by the cointegrating relations among some variables might compensate the exclusion of the capital stock series from the empirical representation of the model.

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    Paper provided by University of Rome La Sapienza, Department of Public Economics in its series Working Papers with number 133.

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    Length: 34
    Date of creation: May 2010
    Date of revision:
    Handle: RePEc:sap:wpaper:wp133
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    1. Ravenna, Federico, 2007. "Vector autoregressions and reduced form representations of DSGE models," Journal of Monetary Economics, Elsevier, vol. 54(7), pages 2048-2064, October.
    2. Francesco Giuli & Massimiliano Tancioni, 2010. "Contractionary Effects of Supply Shocks: Evidence and Theoretical Interpretation," Working Papers 131, University of Rome La Sapienza, Department of Public Economics.
    3. Christopher J. Erceg & Luca Guerrieri, 2004. "Can Long-Run Restrictions Identify Technology Shocks?," Computing in Economics and Finance 2004 3, Society for Computational Economics.
    4. 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.).
    5. Vlaar, Peter J.G., 2004. "On The Asymptotic Distribution Of Impulse Response Functions With Long-Run Restrictions," Econometric Theory, Cambridge University Press, vol. 20(05), pages 891-903, October.
    6. V. V. Chari & Patrick J. Kehoe & Ellen R. McGrattan, 2007. "Are structural VARs with long-run restrictions useful in developing business cycle theory?," Staff Report 364, Federal Reserve Bank of Minneapolis.
    7. Pesaran, M. Hashem & Smith, Ron, 1995. "Estimating long-run relationships from dynamic heterogeneous panels," Journal of Econometrics, Elsevier, vol. 68(1), pages 79-113, July.
    8. Campbell, John, 1994. "Inspecting the Mechanism: An Analytical Approach to the Stochastic Growth Model," Scholarly Articles 3196342, Harvard University Department of Economics.
    9. Warne, A., 1993. "A Common Trends Model: Identification, Estimation and Inference," Papers 555, Stockholm - International Economic Studies.
    10. Edward N. Gamber & Frederick L. Joutz, 1997. "Real Wages over the Business Cycle," Eastern Economic Journal, Eastern Economic Association, vol. 23(3), pages 277-291, Summer.
    11. 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.
    12. Jon Faust & Eric M. Leeper, 1994. "When do long-run identifying restrictions give reliable results?," Working Paper 94-2, Federal Reserve Bank of Atlanta.
    13. King, Robert G. & Plosser, Charles I. & Rebelo, Sergio T., 1988. "Production, growth and business cycles : II. New directions," Journal of Monetary Economics, Elsevier, vol. 21(2-3), pages 309-341.
    14. Cooley, Thomas F. & Dwyer, Mark, 1998. "Business cycle analysis without much theory A look at structural VARs," Journal of Econometrics, Elsevier, vol. 83(1-2), pages 57-88.
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