Recovering Output‐Specific Inputs from Aggregate Input Data: A Generalized Cross‐Entropy Approach
针对多产品企业只有加总投入数据、缺少活动特定投入数据的问题，提出一种广义交叉熵方法，无需利润最大化等行为假设即可估计投入分配，并允许加入行为约束和先验信息。
Abstract For multiproduct firms, data on aggregate input usage are typically available but data on activity‐specific inputs are not. The present study proposes a generalized cross‐entropy approach to estimate activity‐specific input allocations that are consistent with the aggregate information. The proposed method does not require behavioral assumptions (e.g., profit maximization) but does accommodate behavioral restrictions as well as nonsample information about the plausible factor shares across enterprises. Monte Carlo experiments using simulated data for multifactor‐multiproduct firms are used to evaluate the performance of the proposed method.