Formulation and Estimation of Dynamic Factor Demand Equations Under Non-Static Expectations: A Finite Horizon Model

This paper proposes a discrete model of investment behavior that incorporates general nonstatic expectations with a general cost of adjustment technology. The combination of these two features usually leads to a set of highly nonlinear first order conditions for the optimal input plan; the expectational variables work in addition as shift parameters. Consequently, an explicit analytic solution for derived factor demand is in general difficult if not impossible to obtain. Simplifying assumptions on the technology and/or the form of the expectational process are therefore typically made in the literature. In this paper we develop an algorithm for the estimation of flexible forms of derived factor demand equations within the above general setting. By solving the first order conditions numerically at each iteration step this algorithm avoids the need for an explicit analytic solution. In particular we consider a model with a finite planning horizon. The relationship between the optimal input plans of the finite and infinite planning horizon model is explored. Due to the discrete setting of the model the forward looking behavior of investment is brought out very clearly. As a byproduct a consistent framework for the use of anticipation data on planned investment is developed.