Oracle Properties, Bias Correction, and Bootstrap Inference for Adaptive Lasso for Time Series M-Estimators
Journal
Journal of time series analysis
ISSN
0143-9782
ISSN-Digital
1467-9892
Type
journal article
Date Issued
2018-03-01
Author(s)
Abstract
We derive new theoretical results on the properties of the adaptive least absolute shrinkage and selection operator (adaptive lasso) for possibly nonlinear time series models. In particular, we investigate the question of how to conduct inference on the parameters given an adaptive lasso model. Central in this study is the test of the hypothesis that a given adaptive lasso parameter equals zero, which therefore tests for a false positive. To this end we introduce a recentered bootstrap procedure, and show, theoretically and empirically through extensive Monte Carlo simulations, that the adaptive lasso can combine efficient parameter estimation, variable selection, and inference in one step. Moreover, we analytically derive a bias correction factor that is able to significantly improve the empirical coverage of the test on the active variables. Finally, we apply the adaptive lasso and the recentered bootstrap procedure to investigate the relation between the short rate dynamics and the economy, thereby providing a statistical foundation (from a model choice perspective) for the classic Taylor rule monetary policy model.
Language
English
HSG Classification
contribution to scientific community
HSG Profile Area
SEPS - Quantitative Economic Methods
Publisher
Wiley-Blackwell
Publisher place
Oxford [u.a.]
Volume
39
Number
2
Start page
111
End page
128
Eprints ID
251757