Normalization in econometrics
AbstractThe 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.
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Bibliographic InfoPaper provided by Federal Reserve Bank of Atlanta in its series Working Paper with number 2004-13.
Date of creation: 2004
Date of revision:
Other versions of this item:
- NEP-ALL-2004-08-09 (All new papers)
- NEP-ECM-2004-08-09 (Econometrics)
- NEP-ETS-2004-08-09 (Econometric Time Series)
- NEP-HPE-2004-08-09 (History & Philosophy of Economics)
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