Normalization in Econometrics
James Hamilton (),
Daniel Waggoner () and
Tao Zha ()
Econometric Reviews, 2007, vol. 26, issue 2-4, 221-252
The issue of normalization arises whenever two different values for a vector of unknown parameters imply the identical economic model. A normalization implies not just a rule for selecting which among equivalent points to call the maximum likelihood estimate (MLE), but 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 an 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. We illustrate these issues with examples taken from mixture models, structural vector autoregressions, and cointegration models.
Keywords: Cointegration; Local identification; Mixture distributions; Maximum likelihood estimate; Numerical Bayesian methods; Regime-switching; Small sample distributions; Vector autoregressions; Weak identification (search for similar items in EconPapers)
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