Oracle inequalities for high dimensional vector autoregressions

Anders Kock and Laurent Callot ()

Journal of Econometrics, 2015, vol. 186, issue 2, 325-344

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.

Keywords: VAR; LASSO; Adaptive LASSO; Oracle inequality; High-dimensional data (search for similar items in EconPapers)
JEL-codes: C01 C02 C13 C32 (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (99)

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Working Paper: Oracle Inequalities for High Dimensional Vector Autoregressions (2012)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:186:y:2015:i:2:p:325-344

DOI: 10.1016/j.jeconom.2015.02.013

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