Large deviations for posterior distributions on the parameter of a multivariate <InlineEquation ID="IEq1"> <EquationSource Format="TEX">$$\text{ AR}(p)$$</EquationSource> </InlineEquation> process

We prove the large deviation principle for the posterior distributions on the (unknown) parameter of a multivariate autoregressive process with i.i.d. Normal innovations. As a particular case, we recover a previous result for univariate first-order autoregressive processes. We also show that the rate function can be expressed in terms of the divergence between two spectral densities. Copyright The Institute of Statistical Mathematics, Tokyo 2013

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Year of publication:	2013
Authors:	Macci, Claudio ; Trapani, Stefano
Published in:	Annals of the Institute of Statistical Mathematics. - Springer. - Vol. 65.2013, 4, p. 703-719
Publisher:	Springer
Subject:	Large deviation principle \| Spectral density \| Divergence \| Relative entropy

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Type of publication:	Article
Source:	RePEc - Research Papers in Economics

Persistent link: https://ebvufind01.dmz1.zbw.eu/10011000077