LASSO for Stochastic Frontier Models with Many Efficient Firms
将自适应LASSO应用于面板固定效应随机前沿模型，通过带符号约束的加权L1惩罚同时选择最有效企业并估计企业层面无效率参数，估计量具有Oracle性质，收敛速度优于最小二乘虚拟变量估计量。
We apply the adaptive LASSO to select a set of maximally efficient firms in the panel fixed-effect stochastic frontier model. The adaptively weighted <i>L</i><sub>1</sub> penalty with sign restrictions allows simultaneous selection of a group of maximally efficient firms and estimation of firm-level inefficiency parameters with a faster rate of convergence than least squares dummy variable estimators. Our estimator possesses the oracle property. We propose a tuning parameter selection criterion and an efficient optimization algorithm based on coordinate descent. We apply the method to estimate a group of efficient police officers who are best at detecting contraband in motor vehicle stops (i.e., search efficiency) in Syracuse, NY.