is a given function of the exogenous variables z and unknown parameters 9, representing the systematic component of the utility difference, and F is the distribution function of the random component of the utility difference. This paper describes a method of estimating the parameters 9 without assuming any functional form for the distribution function F, and proves that this estimator is consistent. F is also consistently estimated. The method uses maximum likelihood estimation in which the likelihood is maximized not only over the parameter 9 but also over a space which contains all distribution functions.

This paper is concerned with the efficient estimation of structural parameters in closed linear systems of higher order stochastic differential equations when the data are in discrete form and the model generally includes both stock and flow variables. A general existence and uniqueness theorem for the solution of such a system is proved and used in the rigorous derivation of exact discrete models satisfied by the various types of data. It is shown how these models can be used in the computation of various asymptotically efficient estimates obtained by the maximization of the Gaussian likelihood or approximations to it.

The purpose of this article is to provide a deeper empirical insight into the structural change of an industry which is more relevant than that obtained by an analysis based on the traditionally estimated average production function. The main contribution is a long run analysis of technical progress and structural change by means of the short run industry production function introduced by Johansen [13], and based on micro data for individual production units. For that purpose we have developed Johansen’s approach into an operational framework for discrete capacity distributions including a special algorithm for the computation of the short-run industry production function. (This abstract was borrowed from another version of this item.)

For a general paramnetric model we consider the problem of consistently estimating that permissible subset of the parameter space that contains the true parameter point and has smallest dimension. The subset selection is done by means of a model selection criterion of

This paper analyzes bundling decisions of a rnultiproduct monopolist facing uncertain demand. The monopolist sells his products using an auction mechanism and the market is analyzed as a game with incomplete information in which the buyers as well as the seller are strategic agents. With a small number of buyers, a profit maximizing seller will bundle all his output. This makes buyers uniformly worse off compared to the case where the same monopolist does not bundle, in the sense that any buyer is worse off regardless of his demand for the monopolist's outputs. With a larger number of buyers, the seller will have a tendency to unbundle his output and "high-demand" buyers are worse off than they would be if the monopolist bundled his output. "Low-demand" buyers. on the other hand. are always better off when the monopolist unbundles his output, regardless of the number of competing buyers. Despite the fact that "high demand" buyers are the typical purchasers of the monopolist's output, the net effect of increasing the number of buyers is greater market efficiency since bundling creates market inefficiencies both ex post and ex ante.

Humans exhibit a fundamental propensity to interact for social, cultural, technological, and other reasons. In this paper we seek to determine when the propensity to interact, expressed in the form of a spatial externality, becomes strong enough to induce agglomeration. We design an abstract world where the only possible reason for agglomeration is the spatial externality. In this world the uniform population distribution is a steady-state. Then to ask under what circumstances does a spatial externality induce agglomeration is to ask under what circumstances does it induce an instability of the steady-state: anything other than a uniform steady-state implies some agglomeration.