Causal forces are a way of summarizing forecasters' expectations about what will happen to a time series in the future. Contrary to the common assumption for extrapolation, time series are not always subject to consistent forces that point in the same direction. Some are affected by conflicting...

We show that the use of prior information derived from former empirical findings and/or subject matter theory regarding the lag structure of the observable variables together with an AR process for the error terms can produce univariate and single equation models that are intuitively appealing,...

We show that the use of prior information derived from former empirical findings and/or subject matter theory regarding the lag structure of the observable variables together with an AR process for the error terms can produce univariate and single equation models that are intuitively appealing,...

This article presents an algebraic approach to formulating and solving large models for sequential decisions under uncertainty. With this approach, decision analysis optimization methods can be applied to complex decision problems which are generally analyzed in management science practice using...

We introduce a general framework that guides the management scientist's formulation of deterministic models of production processes. Using the framework, we reformulate the constraints of familiar linear programming-based planning models to specifically treat components of production lead time,...