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Understanding the effect of technology shocks in SVARs with long-run restrictions

  • Chaudourne, Jeremy
  • Fève, Patrick
  • Guay, Alain

This paper studies the statistical properties of impulse response functions in structural vector autoregressions (SVARs) with a highly persistent variable as hours worked and long-run identifying restrictions. The highly persistent variable is specified as a nearly stationary persistent process. Such a process appears to be particularly well suited to characterize the dynamics of hours worked because it implies a unit root in a finite sample but is asymptotically stationary and persistent. This is typically the case for per capita hours worked which are included in SVARs. Theoretical results derived from this specification allow us to explain most of the empirical findings from SVARs which include US hours worked.

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Article provided by Elsevier in its journal Journal of Economic Dynamics and Control.

Volume (Year): 41 (2014)
Issue (Month): C ()
Pages: 154-172

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Handle: RePEc:eee:dyncon:v:41:y:2014:i:c:p:154-172
DOI: 10.1016/j.jedc.2014.01.012
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  3. Fève, Patrick & Guay, Alain, 2009. "Identification of Technology Shocks in Structural VARs," TSE Working Papers 09-028, Toulouse School of Economics (TSE).
  4. Christopher J. Erceg & Luca Guerrieri & Christopher Gust, 2005. "Can Long-Run Restrictions Identify Technology Shocks?," Journal of the European Economic Association, MIT Press, vol. 3(6), pages 1237-1278, December.
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  8. Pierre Perron & Serena Ng, 1996. "Useful Modifications to some Unit Root Tests with Dependent Errors and their Local Asymptotic Properties," Review of Economic Studies, Oxford University Press, vol. 63(3), pages 435-463.
  9. Lawrence J. Christiano & Martin Eichenbaum & Charles L. Evans, 2001. "Nominal rigidities and the dynamic effects of a shock to monetary policy," Working Paper Series WP-01-08, Federal Reserve Bank of Chicago.
  10. Yongsung Chang & Taeyoung Doh & Frank Schorfheide, 2007. "Non-stationary Hours in a DSGE Model," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(6), pages 1357-1373, 09.
  11. Gospodinov, Nikolay, 2010. "Inference in Nearly Nonstationary SVAR Models With Long-Run Identifying Restrictions," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(1), pages 1-12.
  12. Lawrence J. Christiano & Martin Eichenbaum & Robert J. Vigfusson, 2006. "Assessing structural VARs," International Finance Discussion Papers 866, Board of Governors of the Federal Reserve System (U.S.).
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  13. Francis, Neville & Ramey, Valerie A., 2005. "Is the technology-driven real business cycle hypothesis dead? Shocks and aggregate fluctuations revisited," Journal of Monetary Economics, Elsevier, vol. 52(8), pages 1379-1399, November.
  14. Galí, Jordi & Rabanal, Pau, 2004. "Technology Shocks and Aggregate Fluctuations: How Well Does the RBC Model Fit Post-War US Data?," CEPR Discussion Papers 4522, C.E.P.R. Discussion Papers.
  15. Blanchard, Olivier Jean & Quah, Danny, 1989. "The Dynamic Effects of Aggregate Demand and Supply Disturbances," American Economic Review, American Economic Association, vol. 79(4), pages 655-73, September.
  16. John G. Fernald, 2012. "A quarterly, utilization-adjusted series on total factor productivity," Working Paper Series 2012-19, Federal Reserve Bank of San Francisco.
  17. Hansen, Gary D., 1985. "Indivisible labor and the business cycle," Journal of Monetary Economics, Elsevier, vol. 16(3), pages 309-327, November.
  18. Ireland, Peter N., 2004. "A method for taking models to the data," Journal of Economic Dynamics and Control, Elsevier, vol. 28(6), pages 1205-1226, March.
  19. Gospodinov, Nikolay & Maynard, Alex & Pesavento, Elena, 2011. "Sensitivity of Impulse Responses to Small Low-Frequency Comovements: Reconciling the Evidence on the Effects of Technology Shocks," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(4), pages 455-467.
  20. Serena Ng & Pierre Perron, 2001. "LAG Length Selection and the Construction of Unit Root Tests with Good Size and Power," Econometrica, Econometric Society, vol. 69(6), pages 1519-1554, November.
  21. Neville Francis & Valerie A. Ramey, 2005. "Measures of Per Capita Hours and their Implications for the Technology-Hours Debate," NBER Working Papers 11694, National Bureau of Economic Research, Inc.
  22. Pantula, Sastry G, 1991. "Asymptotic Distributions of Unit-Root Tests When the Process Is Nearly Stationary," Journal of Business & Economic Statistics, American Statistical Association, vol. 9(1), pages 63-71, January.
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