Identification and Estimation Issues for a Causal Earnings Model
构建了一个企业收益的因果模型，从生产、投资和存货会计规则内生推导，并理论分析了时间序列收益模型的识别与估计问题，为会计和经济学研究者提供方法论参考。
In this paper, I postulate a causal, or structural, model of corporate earnings and present a theoretical analysis of various empirical issues related to the identification and estimation of time-series earnings models.' The postulated model describing corporate earnings is derived endogenously by specifying a model of the firm's production and invest ment structure and its inventory accounting rule. The main characteristic of this model is that the firm uses linear stochastic decision rules to determine its production, inventory, and capital investment levels. Such decision rules are frequently postulated in the economics literature. Using the theoretical properties of the derived earnings model, I then address estimation and forecasting issues in a general manner without actually doing the estimation. Starting with Dopuch and Watts [1972], much of the empirical research in accounting on the structural (i.e., time-series) properties of earnings numbers has first postulated a linear, stochastic, time-series model as the underlying earnings-generating process, and then identified and estimated the model using a sample of realized earnings numbers. These accounting studies usually have the goal of estimating an expectation model to generate expected or forecasted earnings. This type of end