Research & Development and Long-Term Economic Growth: A Bayesian Model Averaging Analysis
We examine the effect of research and development (R&D) on long-term economic growth using the Bayesian model averaging (BMA) to deal rigorously with model uncertainty. Previous empirical studies investigated the effect of dozens of regressors on long-term growth, but they did not examine the effect of R&D due to data unavailability. We extend these studies by proposing to capture the R&D intensity by the number of Nobel prizes in science. Using our indicator, our estimates show that R&D exerts a positive effect on long-term growth with posterior inclusion probability of 0.25 using our preferred parameter and model priors.
|Date of creation:||Jun 2011|
|Date of revision:||Jun 2011|
|Contact details of provider:|| Postal: Opletalova 26, CZ-110 00 Prague|
Phone: +420 2 222112330
Fax: +420 2 22112304
Web page: http://ies.fsv.cuni.cz/
More information through EDIRC
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Xavier Sala-I-Martin & Gernot Doppelhofer & Ronald I. Miller, 2004.
"Determinants of Long-Term Growth: A Bayesian Averaging of Classical Estimates (BACE) Approach,"
American Economic Review,
American Economic Association, vol. 94(4), pages 813-835, September.
- Gernot Doppelhofer & Ronald I. Miller & Xavier Sala-i-Martin, 2000. "Determinants of Long-Term Growth: A Bayesian Averaging of Classical Estimates (Bace) Approach," OECD Economics Department Working Papers 266, OECD Publishing.
- Gernot Doppelhofer & Ronald I. Miller & Xavier Sala-i-Martin, 2000. "Determinants of Long-Term Growth: A Bayesian Averaging of Classical Estimates (BACE) Approach," NBER Working Papers 7750, National Bureau of Economic Research, Inc.
When requesting a correction, please mention this item's handle: RePEc:fau:wpaper:wp2011_19. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Lenka Herrmannova)
If references are entirely missing, you can add them using this form.