Corresponding author: Chris Papageorgiou, International Monetary Fund, 700 19th Street, N.W. Washington, DC 20431, USA. Email: email@example.com.
Growth Empirics without Parameters*
Version of Record online: 23 AUG 2011
© 2011 The Author(s). The Economic Journal © 2011 Royal Economic Society
The Economic Journal
Volume 122, Issue 559, pages 125–154, March 2012
How to Cite
Henderson, D. J., Papageorgiou, C. and Parmeter, C. F. (2012), Growth Empirics without Parameters. The Economic Journal, 122: 125–154. doi: 10.1111/j.1468-0297.2011.02460.x
We thank the editor Antonio Ciccone, two anonymous referees, Mike Delgado and Thanasis Stengos for insightful comments as well as participants of seminars given at Louisiana State University, the University of Nevada, Las Vegas, the 2009 Meeting of the Midwest Econometric Group at Purdue University, the 2009 North American Summer Meeting of the Econometric Society at Boston University, as well as the 2010 World Congress of the Econometric Society at Shanghai Jiao Tong University. We acknowledge superb research assistance from William Larson funded by a research grant from the Research Department of the IMF. The data and code used in this article are available from the authors upon request. The views expressed are the sole responsibility of the authors and should not be attributed to the International Monetary Fund, its Executive Board, or its management.
- Issue online: 5 MAR 2012
- Version of Record online: 23 AUG 2011
- Submitted: 16 May 2008 Accepted: 10 May 2011
Recent research on growth empirics has focused on resolving model and variable uncertainty. The conventional approach has been to assume a linear growth process and then to proceed with investigating the relevant variables that determine cross-country growth. This article questions the linearity assumption underlying the vast majority of such research and uses recently developed non-parametric techniques to handle non-linearities as well as select relevant variables. We show that inclusion of non-linearities is necessary for determining the empirically relevant variables and uncovering key mechanisms of the growth process.