A robust approach to estimating production functions: Replication of the ACF procedure
通过蒙特卡洛模拟复制ACF的生产函数估计方法，发现有限样本中估计值可能与虚假最小值混淆，提出使用额外滞后工具或顺序搜索来获得稳健估计。
Summary We study Ackerberg, Caves, and Frazer's ( Econometrica , 2015, 83 , 2411–2451; hereafter ACF) production function estimation method using Monte Carlo simulations. First, we replicate their results by following their procedure to confirm the existence of a spurious minimum in the estimation, as noted by ACF. In the population, or when sample sizes are sufficiently large, this “global” identification problem may not be a concern because the spurious minimum occurs only at extreme values of capital and labor coefficients. However, in finite samples, their estimator can produce estimates that may not be clearly distinguishable from the spurious ones. In our second experiment, we modify the ACF procedure and show that robust estimates can be obtained using additional lagged instruments or sequential search. We also provide some arguments for why such modifications help in the ACF setting.