Daily Cash Forecasting and Seasonal Resolution: Alternative Models and Techniques for Using the Distribution Approach
提出乘性和混合效应模型来捕捉每日现金流的月内和周内模式，并解决多重季节性分解中的共线性、假日效应等问题，为财务管理者提供更准确的日度现金预测方法。
Daily cash forecasting generally requires some method to reflect day-of-month and dayof-week effects. It requires the resolution of multiple seasonals, a problem given scant treatment in the econometrics literature. This paper first presents multiplticative and mixed-effects specifications of day-of-month and day-of-week effects as alternatives to the additive specifications. Then, several important estimation issues pertinent to each speeifi? cation are investigated, namely collinearity, holiday effects, length-of-month distortion, varying weekly-monthly pattern mix, and daily-monthly consistency. The paper develops a broad class of distribution-based linear forecasting models in great generality similar to the way that Box and Jenkins [1] provide a broad class of timeseries models that can be specialized via parameter selection (speeification). In our case, parameter selection (speeification) gives particular members of the linear class of distribu? tion models. A particular version can be tested against an alternative speeification via hypothesis tests on model parameters.