Structural Models of Factor Demands and Technological Change: An Empirical Assessment of Dynamic Adjustment Specifications for Sectors of the Dutch Economy

In this paper, we combine a translog cost functional form with an adjustment process according to the error correction mechanism to explain the simultaneous determination of factor demands and technological change. To save degrees of freedom in the estimation procedure, we also consider the imposition of restrictions on the matrices of lag parameters and/or the covariance matrix of the disturbances. Using a model selection strategy based on a combination of economic-theoretical considerations and a formal model selection criterion, a model is selected for each of 17 sectors of the Dutch economy. It turns out that, for 14 of the 17 sectors under consideration, a model is chosen that allows the imposition of restrictions with respect to the matrices of lag parameters. A comparison of the present results with those obtained by Lesuis and de Boer reveals that the application of more general dynamic structures leads to results that are more in accordance with economic theory.     

On Bayesian and non-Bayesian estimation of a two-level CES production function for the Dutch manufacturing sector

In this paper maximum-likelihood estimates of the parameters of the two-level CES function, obtained by direct estimation of this function, are given. In addition the authors propose to show how a Bayesian analysis may help to find a solution to the difficulties related with, but not specific to, this particular estimation problem. It is shown that numerical integration of the posterior distribution may give an indication as to which parameter has to be pinpointed and at which value when multi-collinearity precludes unconditional maximization of the likelihood. It is suspected that this approach has a wider field of application.     

Maximum likelihood estimation of sum-constrained linear models with insufficient observations

A maximum likelihood procedure for estimating sum-constrained linear models is presented, which seems to provide a good balance between excessive observational requirements on the one hand and an unduly restrictive specification of the contemporaneous covariance matrix on the other.     

A new approach to maximum likelihood estimation of sum-constrained linear models in case of undersized samples

Maximum likelihood procedures for estimating sum-constrained models like demand systems, brand choice models and so on, break down or produce very unstable estimates when the number of categories ( n ) is large as compared with the number of observations ( T ). In applied research, this problem is usually resolved by postulating the contemporaneous covariance matrix of the dependent variables to be known apart from a constant of proportionality. In this paper we develop a maximum likelihood procedure for sum-constrained models with large numbers of categories, which does not require too many observations, but nevertheless allows for n covariance parameters to be estimated freely.     

A dynamic specification of an AIDS import allocation model

In this paper we use the first-order autoregressive scheme in order to introduce dynamics into the AIDS model. We also consider the theoretical restrictions of additivity, homogeneity and symmetry, and use two different specifications of the covariance matrix. We estimate the models using import allocation data for the UK 1952–1979 of five EEC countries and test different specifications against each other.     

Estimating Input–Output Factor Demand Equations: Some Results on Hanoch's HCDES Production Function

In this paper, we estimate a factor demand system based on Hanoch's homogeneous constant differences of elasticities of substitution production function under a newly proposed specification of the contemporaneous covariance matrix. This admits that the variances of the factor demands may be different but, nevertheless, does not ask for many observations.     

A note on aggregation of CES-type production functions

In this note we shall be concerned with the aggregation of the constant elasticity of substitution (CES) type of production function. In particular we will derive the error made by using the arithmetic averages as they are usually published, rather than the theoretically required averages.     

Estimation and testing of alternative production function models

The paper deals with four different decision models for a firm operating with a constant elasticity of substitution production function. The assumptions underlying the different models and their consequences for estimation are carefully specified. Maximum likelihood estimates of the parameters are obtained of the full four-equation models, using data pertaining to the Dutch manufacturing sector. Finally likelihood ratio test procedures are developed in order to determine whether one or more of the decision models can be rejected on the basis of this data.     

Bayesian limited-information analysis of nonlinear simultaneous equations systems

This paper presents a Bayesian limited-information estimation method that can be used to estimate a single nonlinear equation that forms part of a system of simultaneous equations. The method can be looked upon as the Bayesian counterpart of Amemiya's nonlinear limited-information maximum-likelihood estimator as well as a generalization of Drèze's Bayesian limited-information estimator for linear simultaneous equations systems. The method is illustrated by applying it to the problem of estimating a CES-production function which forms part of a complete model of firm behavior.