Oracle inequalities for high dimensional vector autoregressions

A-Tier
Journal: Journal of Econometrics
Year: 2015
Volume: 186
Issue: 2
Pages: 325-344

Authors (2)

Kock, Anders Bredahl (Oxford University) Callot, Laurent (not in RePEc)

Score contribution per author:

2.011 = (α=2.01 / 2 authors) × 2.0x A-tier

α: calibrated so average coauthorship-adjusted count equals average raw count

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.

Technical Details

RePEc Handle
repec:eee:econom:v:186:y:2015:i:2:p:325-344
Journal Field
Econometrics
Author Count
2
Added to Database
2026-01-25