Adaptive LASSO estimation for ARDL models with GARCH innovations
研究了在条件异方差模型下，自适应LASSO方法能正确选择自回归分布滞后模型中的相关变量，且估计量具有Oracle性质，蒙特卡洛模拟验证了有限样本表现。
In this paper, we show the validity of the adaptive least absolute shrinkage and selection operator (LASSO) procedure in estimating stationary autoregressive distributed lag(p,q) models with innovations in a broad class of conditionally heteroskedastic models. We show that the adaptive LASSO selects the relevant variables with probability converging to one and that the estimator is oracle efficient, meaning that its distribution converges to the same distribution of the oracle-assisted least squares, i.e., the least square 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.