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On the Identification of Technology Shocks: An Alternative to the Standard Long-Run Method
Department of Economics, University of Colorado, Boulder, USA
- 1 Department of Economics, University of Colorado, Boulder, USA
Theoretical Economics Letters·Volume 02 (2012)·Pages 474–481·Published 19 December 2012·DOI10.4236/tel.2012.25089
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Abstract
This study proposes an alternative procedure to identify technology shocks using vector autoregressions (VARs). The proposed procedure delivers improved small-sample properties relative to the standard long-run identification method provided that the dynamics of the observed variables can only be captured precisely by an infinite-order VAR. Monte Carlo experiments on artificial data produced by a standard version of the real business cycle model demonstrate that the proposed procedure is associated with smaller average bias and mean square error. These results obtain under a range of specifications regarding the share of technology shocks in overall output variability.
KeywordsTechnology ShocksLong-Run RestrictionsRelative Identification
- N. Francis and V. A. Ramey, “Is the Technology-Driven Real Business Cycle Hypothesis Dead? Shocks and Aggregate Fluctuations Revisited,” Journal of Monetary Economics, Vol. 52, No. 8, 2005, pp. 1379-1399. doi:10.1016/j.jmoneco.2004.08.009
- N. Francis and V. A. Ramey, “Measures of Per Capita Hours and Their Implications for the Technology-Hours Debate,” Journal of Money, Credit and Banking, Vol. 41, No. 6, 2009, pp. 1071-1097. doi:10.1111/j.1538-4616.2009.00247.x
- J. Gali and P. Rabanal, “Technology Shocks and Aggregate Fluctuations: How Well Does the RBC Model Fit Postwar US Data?” NBER Macroeconomics Annual, Vol. 19, 2004, pp. 225-288.
- U. D. Demirel, “Identification of Technology Shocks Using Misspecified VARs,” University of Colorado, Boulder, 2012.
- E. Mertens, “Are Spectral Estimators Useful for Implementing Long-Run Restrictions in SVARs?” Journal of Economic Dynamics and Control, Vol. 36, No. 12, 2012, pp. 1831-1842. doi:10.1016/j.jedc.2012.06.007
- V. V. Chari, P. J. Kehoe and E. L. McGrattan, “Are Structural VARs with Long-Run Restrictions Useful in Developing Business Cycle Theory?” Journal of Monetary Economics, Vol. 55, No. 8, 2008, pp. 1337-1352. doi:10.1016/j.jmoneco.2008.09.010
- C. J. Erceg, L. Guerrieri and C. Gust, “Can Long-Run Restrictions Identify Technology Shocks?” Journal of the European Economic Association, Vol. 3, No. 6, 2005, pp. 1237-1278. doi:10.1162/154247605775012860
- F. Ravenna, “Vector Autoregressions and Reduced Form Representations of DSGE Models,” Journal of Monetary Economics, Vol. 54, No. 7, 2007, pp. 2048-2064. doi:10.1016/j.jmoneco.2006.09.002
- T. F. Cooley and M. Dywer, “Business Cycle Analysis without Much Theory: A Look at Structural VARs,” Journal of Econometrics, Vol. 83, No. 1-2, 1998, pp. 57-88. doi:10.1016/S0304-4076(97)00065-1
- L. Christiano, M. Eichenbaum and R. Vigfusson, “Alternative Procedures for Estimating Vector Autoregressions Identified with Long-Run Restrictions,” Journal of the European Economic Association, Vol. 4, No. 2-3, 2005, pp. 475-483. doi:10.1162/jeea.2006.4.2-3.475
- L. Christiano, M. Eichenbaum and R. Vigfusson, “Assessing Structural VARs,” NBER Macroeconomics Annual, Vol. 21, 2006, pp. 1-72.
- H. Uhlig, “What Moves Real GNP?” Humboldt University, Berlin, 2003.
- J. Gali, “Technology, Employment, and the Business Cycle: Do Technology Shocks Explain Aggregate Fluctuations?” American Economic Review, Vol. 89, No. 1, 1999, pp. 249-271. doi:10.1257/aer.89.1.249