Formulation and Estimation of Dynamic Factor Demand Equations Under Non-Static Expectations: A Finite Horizon Model / Ingmar R. Prucha, M. Ishaq Nadiri.

By:

Prucha, Ingmar R

Contributor(s):

Material type: Text

TextSeries: Technical Working Paper Series (National Bureau of Economic Research) ; no. t0026.Publication details: Cambridge, Mass. National Bureau of Economic Research 1982.Description: 1 online resource: illustrations (black and white)Online resources:

Available additional physical forms:

Hardcopy version available to institutional subscribers

Abstract: 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.

Tags from this library: No tags from this library for this title. Log in to add tags.

Average rating: 0.0 (0 votes)

Holdings
Item type	Home library	Collection	Call number	Status	Date due	Barcode	Item holds
Working Paper	Biblioteca Digital	Colección NBER	nber t0026 (Browse shelf(Opens below))	Not For Loan

Total holds: 0

Collection: Colección NBER Close shelf browser (Hides shelf browser)

Previous	No cover image available	No cover image available	No cover image available	No cover image available	No cover image available	No cover image available	No cover image available	Next
Previous	nber t0023 Stochastic Capital Theory I. Comparative Statics /	nber t0024 Identification in Dynamic Linear Models with Rational Expectations /	nber t0025 Smoothness Priors and Nonlinear Regression /	nber t0026 Formulation and Estimation of Dynamic Factor Demand Equations Under Non-Static Expectations: A Finite Horizon Model /	nber t0027 The Effect of Ignoring Heteroscedasticity on Estimates of the Tobit Model /	nber t0028 Methods of Solution and Simulation for Dynamic Rational Expectations Models /	nber t0029 Optimal and Time-Consistent Polices in Continuous Time Rational Expectations Models /	Next

October 1982.

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.