On the Identification of Gross Output Production Functions
研究了在常用代理变量方法下总产出生产函数的非参数识别问题，提出一种新策略，即使没有额外价格变动也能识别，并用哥伦比亚和智利工厂数据验证了方法的稳健性。
We study the nonparametric identification of gross output production functions under the environment of the commonly employed proxy variable methods. We show that applying these methods to gross output requires additional sources of variation in the demand for flexible inputs (e.g., prices). Using a transformation of the firm's first-order condition, we develop a new nonparametric identification strategy for gross output that can be employed even when additional sources of variation are not available. Monte Carlo evidence and estimates from Colombian and Chilean plant-level data show that our strategy performs well and is robust to deviations from the baseline setting.