Adaptative LASSO estimation for ARDL models with GARCH innovations

Arbeitspapier

In this paper we show the validity of the adaptive LASSO procedure in estimating stationary ARDL(p,q) models with GARCH innovations. We show that, given a set of initial weights, the adaptive Lasso selects the relevant variables with probability converging to one. Afterwards, we show that the estimator is oracle, meaning that its distribution converges to the same distribution of the oracle assisted least squares, i.e., the least squares estimator calculated as if we knew the set of relevant variables beforehand. Finally, we show that the LASSO estimator can be used to construct the initial weights. The performance of the method in finite samples is illustrated using Monte Carlo simulation

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Texto para discussão ; No. 637

Classification and Topics

• Classification: Wirtschaft ; Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
• Subject: ARDL ; GARCH ; sparse models ; shrinkage ; LASSO ; adaLASSO ; time series

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Medeiros, Marcelo C. ; Mendes, Eduardo F.
• Event: Veröffentlichung
• (who): Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio), Departamento de Economia
• (where): Rio de Janeiro
• (when): 2015

Further information

• Handle: http://hdl.handle.net/10419/176120
• Last update: 10.03.2025, 11:43 AM CET