Hostname: page-component-8448b6f56d-tj2md Total loading time: 0 Render date: 2024-04-23T19:46:08.275Z Has data issue: false hasContentIssue false
  • English
  • Français

THE HOURS WORKED–PRODUCTIVITY PUZZLE: IDENTIFICATION IN A FRACTIONAL INTEGRATION SETTING

Published online by Cambridge University Press:  04 April 2014

Yuliya Lovcha  and
Alejandro Perez-Laborda
Yuliya Lovcha*
Affiliation:
Universidad de Navarra
Alejandro Perez-Laborda
Affiliation:
Universitat Rovira-i-Virgili and CREIP
*
Address correspondence to: Yuliya Lovcha, Departamento de Economia, Universidad de Navarra, 31080 Pamplona, Spain; e-mail: yuliya.lovcha@gmail.com.
Get access Rights & Permissions [Opens in a new window]

Abstract

A recent finding of the SVAR literature is that the response of hours worked to a (positive) technology shock depends on the assumed order of integration of the hours. In this work we relax this assumption, allowing fractional integration in hours and productivity. We find that the sign and magnitude of the estimated responses depend crucially on the identification assumptions employed. Although the responses of hours recovered with short-run (SR) restrictions are positive in all data sets, long-run (LR) identification results in negative, although sometimes not significant responses. We check the validity of these assumptions with the Sims procedure, concluding that both LR and SR are appropriate to recover responses in a fractionally integrated VAR. However, the application of the LR scheme always results in an increase in sampling uncertainty. Results also show that even the negative responses found in the data could still be compatible with real business cycle models.

Keywords

VARFIMATechnology ShockLong MemoryStructural VAR
Type
Articles
Information
Macroeconomic Dynamics , Volume 19 , Issue 7 , October 2015 , pp. 1593 - 1621
Copyright
Copyright © Cambridge University Press 2014 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Berkowitz, Jeremy and Diebold, Francis X. (1998) Bootstrapping multivariate spectra. Review of Economics and Statistics 80, 664–666.CrossRefGoogle Scholar
Blanchard, Oliver J. and Quah, Danny (1989) The dynamic effects of aggregate demand and supply disturbances. American Economic Review 79, 655–673.Google Scholar
Boes, Duane C., Davis, Richard A., and Gupta, Sat N. (1989) Parameter estimation in low order fractionally differenced ARMA processes. Stochastic Hydrology and Hydraulics 3, 97–110.CrossRefGoogle Scholar
Canova, Fabio, Lopez-Salido, David J., and Michelacci, Claudio (2007) The Labor Market Effects of Technology Shocks. Documento de trabajo 0719, Banco de España.CrossRefGoogle Scholar
Chari, V.V., Patrick, J. Kehoe and McGrattan, Ellen R. (2008) Are structural VARs with long-run restrictions useful in developing business cycle theory? Journal of Monetary Economics 55, 1337–1352.CrossRefGoogle Scholar
Christiano, Lawrence J., Martin Eichenbaum, and Robert Vigfusson (2003) What Happens after a Technology Shock? FRB international finance discussion paper 768, Board of Governors of the Federal Reserve System.CrossRefGoogle Scholar
Christiano, Lawrence J., Eichenbaum, Martin, and Vigfusson, Robert (2007) Assessing structural VARs. In Acemoglu, D., Rogoff, K., and Woodford, M. (eds.), NBER Macroeconomics Annual 2006, Vol. 21, pp. 1–106. Cambridge, MA: MIT Press.Google Scholar
Erceg, Christopher J., Guerrieri, Luca, and Gust, Christopher (2005) Can long-run restrictions identify technology shocks? Journal of the European Economic Association 3, 1237–1278.CrossRefGoogle Scholar
Faust, Jon and Leeper, Eric M. (1997) When do long-run identifying restrictions give reliable results? Journal of Business and Economic Statistics 15, 345–353.Google Scholar
Fernald, John G. (2007) Trend breaks, long-run restrictions, and contractionary technology improvements. Journal of Monetary Economics 54, 2467–2485CrossRefGoogle Scholar
Fernandez-Villaverde, Jesus, Rubio-Ramirez, Juan, Sargent, Thomas J., and Watson, Mark W. (2007) A, B, C's (and D's) for understanding VARs. American Economic Review 97, 1021–1026.CrossRefGoogle Scholar
Francis, Neville and Ramey, Valerie A. (2009) Measures of per capita hours and their implications for the technology–hours debate. Journal of Money, Credit and Banking 41, 1071–1097.CrossRefGoogle Scholar
Gali, Jordi (1999) Technology, employment, and the business cycle: Do technology shocks explain aggregate fluctuations? American Economic Review 89 (1), 249–271.CrossRefGoogle Scholar
Gali, Jordi and Pau Rabanal (2005) Technology shock and aggregate fluctuations: How well does the RBC model fit post war US data? In Gertler, M. and Rogoff, K. (eds.), NBER Macroeconomics Annual 2004, Vol. 19, pp. 225–318. Cambridge, MA: MIT Press.Google Scholar
Gil-Alana, Luis A. and Moreno, Antonio (2009) Technology shocks and hours worked: A fractional integration perspective. Macroeconomic Dynamics 13, 580–604.CrossRefGoogle Scholar
Hosoya, Yuzo (1996) The quasi-likelihood approach to statistical inference on multiple time-series with long-range dependence. Journal of Econometrics 73, 217–236.CrossRefGoogle Scholar
Lobato, Ignacio N. (1999) A semiparametric two-step estimator in a multivariate long memory model. Journal of Econometrics 90, 129–153.CrossRefGoogle Scholar
Pesavento, Elena and Rossi, Barbara, (2005) Do technology shocks drive hours up or down? A little evidence from an agnostic procedure. Macroeconomic Dynamics 9, 478–488.CrossRefGoogle Scholar
Rubio-Ramirez, Juan F., Waggoner, Daniel F., and Zha, Tao (2010) Structural vector autoregressions: Theory of identification and algorithms for inference. Review of Economic Studies 77, 665–696.CrossRefGoogle Scholar
Sims, Christopher (1972) The role of approximate prior restrictions in distributed lag estimation. Journal of the American Statistical Association 67, 169–175.CrossRefGoogle Scholar
Sims, Christopher (1989) Models and their uses. American Journal of Agricultural Economics 71, 489–494.CrossRefGoogle Scholar
Sims, Christopher and Zha, Tao (2006) Does monetary policy generate recessions? Macroeconomic Dynamics 10, 231–272.CrossRefGoogle Scholar
Sowell, Fallaw B. (1992), Maximum likelihood estimation of stationary fractionally integrated time series. Journal of Econometrics 53, 165–188.CrossRefGoogle Scholar
Tschernig, Rolf, Weber, Enzo, and Weigand, Roland (2013) Long-run identification in fractionally integrated system. Journal of Business and Economic Statistics 31, 438–450.CrossRefGoogle Scholar
Uhlig, Harald (2004) Do technology shocks lead to a fall in total hours worked? Journal of the European Economic Association 2, 361–371.CrossRefGoogle Scholar