Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/176120 
Year of Publication: 
2015
Series/Report no.: 
Texto para discussão No. 637
Publisher: 
Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio), Departamento de Economia, Rio de Janeiro
Abstract: 
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
Subjects: 
ARDL
GARCH
sparse models
shrinkage
LASSO
adaLASSO
time series
JEL: 
C22
Document Type: 
Working Paper

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