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URL :  Normalization in Econometrics
Author : Multiple Authors
Detail : eBook Edition: 2004

The issue of normalization arises whenever two different values for a vector of unknown parameters imply the identical economic model. 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 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. It illustrates these issues with examples taken from mixture models, structural vector auto regressions, and cointegration models.

eBook Contents

Introduction – Normalization and an identification principle – Mixture models – Structural vars – Cointegration – Conclusions and recommendations for applied research – Appendix – Acknowledgement – References

eBook License: Copyrighted (Personal Use Only)

Free PDF eBook - 1,1 Mb - 70 pages
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