Working paper

Understanding the Effect of Technology Shocks in SVARs with Long-Run Restrictions

Jeremy Chaudourne, and Patrick Fève


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 process appears particularly well suited to characterized the dynamics of hours worked because it implies a unit root in 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 to explain most of the empirical findings from SVARs which include U.S. hours worked. Simulation experiments from an estimated DSGE model confirm theoretical results.


SVARs; long-run restrictions; locally nonstationary process; technology shocks; hours worked;

JEL codes

  • C32: Time-Series Models • Dynamic Quantile Regressions • Dynamic Treatment Effect Models • Diffusion Processes
  • E32: Business Fluctuations • Cycles

Replaced by

Jeremy Chaudourne, Patrick Fève, and Alain Guay, Understanding the effect of technology shocks in SVARs with long-run restrictions, Journal of Economic Dynamics and Control, vol. 41, April 2014, pp. 154–172.


Jeremy Chaudourne, and Patrick Fève, Understanding the Effect of Technology Shocks in SVARs with Long-Run Restrictions, TSE Working Paper, n. 12-331, August 2012.

See also

Published in

TSE Working Paper, n. 12-331, August 2012