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Oracle inequalities for high dimensional vector autoregressions

Abstract:

This paper establishes non-asymptotic oracle inequalities for the prediction error and estimation accuracy of the LASSO in stationary vector autoregressive models. These inequalities are used to establish consistency of the LASSO even when the number of parameters is of a much larger order of magnitude than the sample size. We also state conditions under which no relevant variables are excluded.

Next, non-asymptotic probabilities are given for the a...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1016/j.jeconom.2015.02.013

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Institution:
University of Oxford
Division:
SSD
Department:
Economics
Oxford college:
St Hilda's College
Role:
Author
Publisher:
Elsevier Publisher's website
Journal:
Journal of Econometrics Journal website
Volume:
186
Issue:
2
Pages:
325-344
Publication date:
2015-03-03
Acceptance date:
2015-01-01
DOI:
ISSN:
0304-4076
Source identifiers:
794885
Keywords:
Pubs id:
pubs:794885
UUID:
uuid:33a9abb0-e0aa-4561-8f6e-bcb36fa8072d
Local pid:
pubs:794885
Deposit date:
2019-04-02

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