Hostname: page-component-586b7cd67f-t8hqh Total loading time: 0 Render date: 2024-11-24T04:26:31.230Z Has data issue: false hasContentIssue false
  • English
  • Français

THE ROLE OF SECTORAL SHIFTS IN THE DECLINE OF REAL GDP VOLATILITY

Published online by Cambridge University Press:  30 January 2012

Daniel Burren  and
Klaus Neusser
Daniel Burren
Affiliation:
University of Bern
Klaus Neusser*
Affiliation:
University of Bern
*
Address correspondence to: Klaus Neusser, Department of Economics, University of Bern, Schanzeneckstrasse 1, P.O. Box 8573, CH-3001 Berne, Switzerland; e-mail: klaus.neusser@vwi.unibe.ch.
Get access Rights & Permissions [Opens in a new window]

Abstract

U.S. production has shifted from goods-producing to service-producing industries. We assess whether this shift contributed to the decline in U.S. output volatility over the period 1949–2005 and provide an estimate of its relative importance. Growth rates of GDP by industry are analyzed in a seemingly unrelated multivariate autoregression framework with time-varying innovation covariance matrices. These changing unobserved covariance matrices are modeled as a Wishart autoregressive process of order one, which results in a nonlinear state-space system. The particle filter is used to obtain estimates of the innovation covariance matrix at each point in time. Several counterfactual experiments make it possible to apportion the decline in output volatility between the shift in the sectoral composition and changes in innovations. Our main finding is that the shift into the service sector can explain about 30% of the decline in GDP's volatility, despite the fact that some sectors became even more volatile. This result is robust across a wide variety of alternative specifications.

Keywords

Sectoral ShiftsGreat ModerationStochastic VolatilityWishart Autoregressive ProcessParticle Filter
Type
Articles
Information
Macroeconomic Dynamics , Volume 17 , Issue 3 , April 2013 , pp. 477 - 500
Copyright
Copyright © Cambridge University Press 2012

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

Sungbae, An (2005) Bayesian Estimation of DSGE Models: Lessons from Second-Order Approximations. Mimeo.Google Scholar
Andrews, Donald W.K. and Ploberger, Werner (1994) Optimal tests when a nuisance parameter is present only under the alternative. Econometrica 62, 1383–1414.CrossRefGoogle Scholar
Arulampalam, M. Sanjeww M., Maskell, Simon and Gordon, Neil and Clapp, Tim (2002) A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Transactions on Signal Processing 50, 174–188.CrossRefGoogle Scholar
Ray, Barrell and Gottschalk, Sylvia (2004) The volatility of the output gap in the G7. National Institute Economic Review 188, 100–107.Google Scholar
Ben, S. Bernanke (1983) Irreversibility, uncertainty and cyclical investment. Quarterly Journal of Economics 98, 85–106.Google Scholar
Blackburn, Keith and Galindev, Ragchaasuren (2003) Growth, volatility and learning. Economics Letters 79, 417–421.CrossRefGoogle Scholar
Blanchard, Olivier and Simon, John (2001) The long and large decline in U.S. output volatility. Brookings Papers on Economic Activity 2001 (1), 135–166.CrossRefGoogle Scholar
Engle, Robert F. and Kroner, Kenneth F. (1993) Multivariate simultaneous generalized ARCH. Econometric Theory 11 (1), 122–150.CrossRefGoogle Scholar
Fang, Wen-Shw. and Miller, Stephen M. (2008) The Great Moderation and the relationship between output growth and its volatility. Southern Economic Journal 74 (3), 819–838.CrossRefGoogle Scholar
Fernandez-Villaverde, Jesus and Rubio-Ramirez, Juan F. (2007) Estimating macroeconomic models: A likelihood approach. Review of Economic Studies 74, 1059–1087.CrossRefGoogle Scholar
Galí, Jordi and Gambetti, Luca (2009) On the sources of the Great Moderation. American Economic Journal: Macroeconomics 1 (1), 26–57.Google Scholar
Gourieroux, Christian, Jasiak, Joann and Sufana, Razvan (2009) The Wishart autoregressive process of multivariate stochastic volatility. Journal of Econometrics 150 (2), 167–181.CrossRefGoogle Scholar
Inclán, Carla and Tiao, George C. (1994) Use of cumulative sums of squares for retrospective detection of changes of variance. Journal of the American Statistical Association 89, 913–923.Google Scholar
Kim, Chang-Ji. and Nelson, Charles R. (1999) State-Space Models with Regime Switching. Cambridge, MA: MIT Press.Google Scholar
Martin, Philppe and Rogers, Carol Ann (1987) Long-term growth and short-term economic instability. European Economic Review 49, 359–381.Google Scholar
McConnell, Margret M. and Perez-Quiros, Gabriel (2000) Output fluctuations in the United States: What has changed since the early 1980's? American Economic Review 90, 1464–1476.CrossRefGoogle Scholar
Neusser, Klaus (2008) Interdependencies of US manufacturing sectoral TFPs: A spatial VAR approach. Journal of Macroeconomics 30, 991–1004.CrossRefGoogle Scholar
Pindyck, Robert S. (1991) Irreversibility, uncertainty and investment. Journal of Economic Literature 29, 1110–1148.Google Scholar
Primiceri, Giorgio E. (2005) Time varying structural vector autoregressions and monetary policy. Review of Economic Studies 72, 821–852.CrossRefGoogle Scholar
Stock, James H. and Watson, Mark W. (2005a) Implications of dynamic factor models for VAR analysis. Mimeo.CrossRefGoogle Scholar
Stock, James H. and Watson, Mark W. (2005b) Understanding changes in international business cycle dynamics. Journal of the European Economic Association 5, 968–1006.CrossRefGoogle Scholar
Tse, Y.K. and Tsui, Albert K.C. (2002) A multivariate generalized autoregressive conditional heteroskedasticity model with time-varying correlations. Journal of Business and Economic Statistics 20 (3), 351–362.CrossRefGoogle Scholar