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Consistent and conservative model selection with the adaptive Lasso in stationary and nonstationary autoregressions

Abstract:

We show that the adaptive Lasso is oracle efficient in stationary and nonstationary autoregressions. This means that it estimates parameters consistently, selects the correct sparsity pattern, and estimates the coefficients belonging to the relevant variables at the same asymptotic efficiency as if only these had been included in the model from the outset. In particular, this implies that it is able to discriminate between stationary and nonstationary autoregressions and it thereby constitute...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1017/S0266466615000304

Authors


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Institution:
University of Oxford
Division:
SSD
Department:
Economics
Oxford college:
St Hilda's College
Role:
Author
Publisher:
Cambridge University Press Publisher's website
Journal:
Econometric Theory Journal website
Volume:
32
Issue:
01
Pages:
243-259
Publication date:
2015-09-01
DOI:
EISSN:
1469-4360
ISSN:
0266-4666
Source identifiers:
794878
Keywords:
Pubs id:
pubs:794878
UUID:
uuid:558d9039-d3d7-420e-8198-864da938450e
Local pid:
pubs:794878
Deposit date:
2019-01-31

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