Normalization in econometrics

James Hamilton (), Daniel F. Waggoner and Tao Zha ()

No 2004-13, Working Paper from Federal Reserve Bank of Atlanta

Abstract: The issue of normalization arises whenever two different values for a vector of unknown parameters imply the identical economic model. A normalization does not just imply a rule for selecting which point, among equivalent ones, to call the maximum likelihood estimator (MLE). It also governs the topography of the set of points that go into a small-sample confidence interval associated with that MLE. A poor normalization can lead to multimodal distributions, disjoint confidence intervals, and very misleading characterizations of the true statistical uncertainty. This paper introduces the identification principle as a framework upon which a normalization should be imposed, according to which the boundaries of the allowable parameter space should correspond to loci along which the model is locally unidentified. The authors illustrate these issues with examples taken from mixture models, structural VARs, and cointegration.

New Economics Papers: this item is included in nep-ecm, nep-ets and nep-hpe
Date: 2004
View list of references View citations in EconPapers

Downloads: (external link)
http://www.frbatlanta.org/filelegacydocs/wp0413.pdf (application/pdf)

Related works:
Journal Article: Normalization in Econometrics (2007)
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: http://EconPapers.repec.org/RePEc:fip:fedawp:2004-13

Ordering information: This working paper can be ordered from

Access Statistics for this paper

More papers in Working Paper from Federal Reserve Bank of Atlanta
Contact information at EDIRC.
Series data maintained by Diane Rosenberger ().