A comparison of two model averaging techniques with an application to growth empirics

Jan Magnus (), Owen Powell and Patricia Prufer ()

Journal of Econometrics, 2010, vol. 154, issue 2, 139-153

Abstract: Parameter estimation under model uncertainty is a difficult and fundamental issue in econometrics. This paper compares the performance of various model averaging techniques. In particular, it contrasts Bayesian model averaging (BMA) -- currently one of the standard methods used in growth empirics -- with a new method called weighted-average least squares (WALS). The new method has two major advantages over BMA: its computational burden is trivial and it is based on a transparent definition of prior ignorance. The theory is applied to and sheds new light on growth empirics where a high degree of model uncertainty is typically present.

Keywords: Model; averaging; Bayesian; analysis; Growth; determinants (search for similar items in EconPapers)
Date: 2010
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (247)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0304-4076(09)00166-3
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:154:y:2010:i:2:p:139-153

Access Statistics for this article

Journal of Econometrics is currently edited by T. Amemiya, A. R. Gallant, J. F. Geweke, C. Hsiao and P. M. Robinson

More articles in Journal of Econometrics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().