Download full text
(613.6Kb)
Citation Suggestion
Please use the following Persistent Identifier (PID) to cite this document:
https://nbn-resolving.org/urn:nbn:de:0168-ssoar-262608
Exports for your reference manager
A comparison of two model averaging techniques with an application to growth empirics
[journal article]
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 — w... view more
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.... view less
Classification
Economic Statistics, Econometrics, Business Informatics
Free Keywords
C51; C52; C13; C11; Model averaging; Bayesian analysis; Growth determinants
Document language
English
Publication Year
2009
Page/Pages
p. 139-153
Journal
Journal of Econometrics, 154 (2009) 2
DOI
https://doi.org/10.1016/j.jeconom.2009.07.004
Status
Postprint; peer reviewed
Licence
PEER Licence Agreement (applicable only to documents from PEER project)