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@article{ Magnus2009,
 title = {A comparison of two model averaging techniques with an application to growth empirics},
 author = {Magnus, Jan R. and Powell, Owen and Prüfer, Patricia},
 journal = {Journal of Econometrics},
 number = {2},
 pages = {139-153},
 volume = {154},
 year = {2009},
 doi = {https://doi.org/10.1016/j.jeconom.2009.07.004},
 urn = {https://nbn-resolving.org/urn:nbn:de:0168-ssoar-262608},
 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.},
}