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Efficient inference about the tail weight in multivariate Student $t$ distributions by Ley, Christophe; Neven, Anouk is a scholarly article available to read on EtoBox.

What is Efficient inference about the tail weight in multivariate Student $t$ distributions about?

We propose a new testing procedure about the tail weight parameter of multivariate Student $t$ distributions by having recourse to the Le Cam methodology. Our test is asymptotically as efficient as the classical likelihood ratio test, but outperforms the latter by its flexibility and simplicity: indeed, our approach allows to estimate the location and scatter nuisance parameters by any root-$n$ consistent estimators, hereby avoiding numerically complex maximum likelihood estimation. The finite-sample properties of our test are analyzed in a Monte Carlo simulation study, and we apply our method on a financial data set. We conclude the paper by indicating how to use this framework for efficient point estimation.

Author

Ley, Christophe; Neven, Anouk

Published

2013

Language

EN