Hostname: page-component-8448b6f56d-m8qmq Total loading time: 0 Render date: 2024-04-19T22:18:29.943Z Has data issue: false hasContentIssue false
  • English
  • Français

DO TECHNOLOGY SHOCKS DRIVE HOURS UP OR DOWN? A LITTLE EVIDENCE FROM AN AGNOSTIC PROCEDURE

Published online by Cambridge University Press:  25 October 2005

ELENA PESAVENTO  and
BARBARA ROSSI
ELENA PESAVENTO
Affiliation:
Emory University
BARBARA ROSSI
Affiliation:
Duke University
Get access Rights & Permissions [Opens in a new window]

Abstract

This paper analyzes the robustness of the estimate of a positive productivity shock on hours to the presence of a possible unit root in hours. Estimations in levels or in first differences provide opposite conclusions. We rely on an agnostic procedure in which the researcher does not have to choose between a specification in levels or in first differences. We find that a positive productivity shock has a negative impact effect on hours, but the effect is much shorter lived, and disappears after two quarters. The effect becomes positive at business-cycle frequencies, although it is not significant.

Keywords

Technology ShocksPositive ProductivityHours Worked
Type
ARTICLES
Information
Macroeconomic Dynamics , Volume 9 , Issue 4 , September 2005 , pp. 478 - 488
Copyright
© 2005 Cambridge University Press

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

Anderson T.W. & Herman Rubin 1949 Estimation of the parameters of a single equation in a complete system of stochastic equations. Annals of Mathematical Statistics 20, 46– 63.Google Scholar
Christiano Larry, Martin Eichenbaum & Robert Vigfusson 2003 What Happens After a Technology Shock? Mimeo, Northwestern University.
Elliott Graham & Michael Jansson 2003 Testing for unit roots with stationary covariates. Journal of Econometrics, 115, 75– 89.Google Scholar
Elliott Graham & James H. Stock 2001 Confidence intervals for autoregressive coefficients near one. Journal of Econometrics 103, 155– 181.Google Scholar
Elliott Graham, Thomas J. Rothenberg & James H. Stock 1996 Efficient tests for an autoregressive unit root. Econometrica 64, 813– 836.Google Scholar
Elliott Graham, Michael Jansson & Elena Pesavento 2005 Optimal power for testing potential cointegrating vectors with known parameters for nonstationarity. Journal of Business and Economic Statistics 23, 34– 38.Google Scholar
Francis Neville & Valerie Ramey 2001 Is the Technology-Driven Real Business Cycle Hypothesis Dead? Shocks and Aggregate Fluctuations Revisited. Mimeo, University of California at San Diego.
Francis Neville, Michael Owyang & Athena T. Theodorou 2003 The use of long-run restrictions for the identification of technology shocks. Federal Reserve Bank of St. Louis Review 85 (6), 53– 66.Google Scholar
Gali Jordi 1999 Technology, employment, and the business cycle: Do technology shocks explain aggregate fluctuations? American Economic Review 89, 249– 271.Google Scholar
Gali Jordi & Paul Rabanal 2004 Technology shocks and aggregate fluctuations: How well does the RBC model fit postwar U.S. data? In Mark Gertler & Kenneth Rogoff (eds.), NBER Macroeconomic Annual, pp. 225– 288. Cambridge, MA: MIT Press.
Hamilton James D. 1994 Time Series Analysis. Princeton, NJ: Princeton University Press.
Hansen Bruce 1995 Rethinking the univariate approach to unit root testing: Using covariates to increase power. Econometric Theory 11, 1148– 1171.Google Scholar
Kilian Lutz & Pao-Li Chang 2000 How accurate are confidence intervals for impulse responses in large VAR models? Economics Letters 69, 299– 307.Google Scholar
Lütkepohl Helmut 1993 Introduction to Multiple Time Series Analysis. New York: Springer-Verlag.
Pesavento Elena & Barbara Rossi 2003 Small sample confidence intervals for multivariate impulse response functions at long Horizons. Working paper 03–19, Duke University.
Shea John 1999 What do technology shocks do? In Ben Bernanke & Julio Rotemberg (eds.), NBER Macroeconomics Annual, pp. 275– 310. Cambridge, MA: MIT Press.
Sims Chris & Harald Uhlig 1991 Understanding unit rooters: A helicopter tour. Econometrica 59, 1591– 1599.Google Scholar
Stock James H. 1991 Confidence intervals for the largest autoregressive root in U.S. macroeconomic time series. Journal of Monetary Economics 28, 435– 459.Google Scholar
Uhlig Harald 2003 Do Technology Shocks Lead to a Fall in Total Hours Worked? Mimeo, Umbolt University.
Vigfusson Robert 2004 The Delayed Response to a Technology Shock: A Flexible Price Explanation. Discussion paper 810, Board of Governors of the Federal Reserve Board International Finance.Google Scholar
Wright Jonathan 2000 Confidence intervals for univariate impulse responses with a near unit root. Journal of Business and Economic Statistics 18, 368– 373.Google Scholar