Penalized estimation of panel vector autoregressive models: A panel LASSO approach
提出一种专门用于面板向量自回归模型的LASSO估计方法，通过惩罚项实现滞后选择、系数同质化及跨方程差异化惩罚，模拟和实证表明其预测精度优于普通最小二乘法。
This paper proposes LASSO estimation specific for panel vector autoregressive (PVAR) models. The penalty term allows for shrinkage for different lags, for shrinkage towards homogeneous coefficients across panel units, for penalization of lags of variables belonging to another cross-sectional unit, and for varying penalization across equations. The penalty parameters therefore build on time series and cross-sectional properties that are commonly found in PVAR models. Simulation results point towards advantages of using the proposed LASSO for PVAR models over ordinary least squares in terms of forecast accuracy. An empirical forecasting application including 20 countries supports these findings.