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含零无效率质量点的随机前沿惩罚筛估计AI 解读

Summary Stochastic frontier models for cross‐sectional data typically assume that the one‐sided distribution of firm‐level inefficiency is continuous. However, it may be reasonable to hypothesize that inefficiency is continuous except for a discrete mass at zero capturing fully efficient firms (zero‐inefficiency). We propose a sieve‐type density estimator for such a mixture distribution in a nonparametric stochastic frontier setting under a unimodality‐at‐zero assumption.…

存在大量最高效率企业时的随机前沿 LASSO 估计AI 解读

We apply the adaptive LASSO to select a set of maximally efficient firms in the panel fixed-effect stochastic frontier model. The adaptively weighted L1 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…

含选择机制的内生网络生产函数AI 解读

We consider a production function that transforms inputs into outputs through peer effect networks. The distinguishing features of this model are that the network is formal and observable through worker scheduling, and selection into the network is done by a manager. We discuss identification and suggest several estimation techniques. We tackle endogeneity arising from selection into groups and exposure to common group factors by employing a polychotomous Heckman-type…

摘要暂缺

与最优者的多重比较及其经济应用AI 解读

In this paper we discuss a statistical method called multiple comparisons with the best, or MCB. This paper is meant to introduce MCB to economists. We discuss possible uses of MCB in economics. The application that we treat in most detail is the construction of confidence intervals for inefficiency measures from stochastic frontier models with panel data. We also consider an application to the analysis of labour market wage gaps. Copyright � 2000 John Wiley & Sons, Ltd.