标题： 具有潜在组结构的生产函数估计的分类器-套索方法 显示英文标题

标题： A Classifier-Lasso Approach for Estimating Production Functions with Latent Group Structures

摘要： 我提出了一种新的生产函数估计方法，该方法考虑了潜在的群体结构。 我考虑的是在不同群体之间具有异质性但在群体内部随时间保持不变的生产函数，并且企业的群体归属是未知的。 我的估计方法是完全数据驱动的，并将生产函数文献中的最新识别策略嵌入到分类Lasso中。 模拟实验表明，企业被分配到正确潜在群体的概率接近于一。 我将我的估计方法应用于智利企业的面板数据，发现与按行业分类的标准方法相比，估计结果存在显著差异。 显示英文摘要

摘要： I present a new estimation procedure for production functions with latent group structures. I consider production functions that are heterogeneous across groups but time-homogeneous within groups, and where the group membership of the firms is unknown. My estimation procedure is fully data-driven and embeds recent identification strategies from the production function literature into the classifier-Lasso. Simulation experiments demonstrate that firms are assigned to their correct latent group with probability close to one. I apply my estimation procedure to a panel of Chilean firms and find sizable differences in the estimates compared to the standard approach of classification by industry.