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?: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. (xsd:string)
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?:dateModified
  • 2009 (xsd:gyear)
?:datePublished
  • 2009 (xsd:gyear)
?:doi
  • 10.1016/j.jeconom.2009.07.004 ()
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  • true (xsd:boolean)
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  • en (xsd:string)
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?:issueNumber
  • 2 (xsd:string)
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  • A comparison of two model averaging techniques with an application to growth empirics (xsd:string)
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  • Zeitschriftenartikel (xsd:string)
  • journal_article (en)
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  • GESIS-SSOAR (xsd:string)
  • In: Journal of Econometrics, 154, 2009, 2, 139-153 (xsd:string)
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?:urn
  • urn:nbn:de:0168-ssoar-262608 ()
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  • 154 (xsd:string)