We present estimators for nonparametric functions that are nonadditive in unobservable random terms. The distributions of the unobservable random terms are assumed to be unknown. We show that when a nonadditive, nonparametric function is strictly monotone in an unobservable random term, and it satisfies some other properties that may be implied by economic theory, such as homogeneity of degree one or separability, the function and the distribution of the unobservable random term are identified. We also present convenient normalizations, to use when the properties of the function, other than strict monotonicity in the unobservable random term, are unknown. The estimators for the nonparametric function and for the distribution of the unobservable random term are shown to be consistent and asymptotically normal. We extend the results to functions that depend on a multivariate random term. The results of a limited simulation study are presented.

Prepared under Contract no. EX-76-A-01-2295, Task order 37.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Economics and Social Science, 1962.

Edited by L.H. Klaassen, L.M. Koyck and H.J. Witteveen, Presented on the occasion of his 25 years jubilee as a professor at the Netherlands School of Economics at Rotterdam. (With a bibliography of professor Tinbergen's work).

The sequential nature of activities like research, development, or exploration requires optimal funding criteria to take account of the fact that subsequent funding decisions will be made throughout the future. Thus, there is a continual possibility of reviewing a project's status, based on the latest information. After setting up a model to capture this feature, optimal funding criteria are investigated. In an important special case, an explicit formula is derived. As well as throwing light upon the nature of development activities, the analysis is also relevant to the general theory of information gathering processes.

In this paper we analyze an aggregative general equilibriiri model in which the use of money is motivated by a cash-in-advance constraint, applied to purchases of a subset of consumption goods. The system is subject to both real and nxnetary shocks, which are economy-wide and observed by all. We develop methods for verifying the existence of, characterizing, and explicitly calculating equilibria. A main result of the analysis is that current money growth affects the current real allocation only insofar as it affects expectations about future money growth, i.e., only through its value as a signal.

This paper integrates the classic theory of firm boundaries, through span of control or taste for variety, into a model of the labor market with random matching and on‐the‐job search. Firms choose when to enter and exit, whether to create vacancies or destroy jobs in response to shocks, and Bertrand‐compete to hire and retain workers. Tractability is obtained by proving that, under a parsimonious set of assumptions, all worker and firm decisions are characterized by their joint surplus, which in turn only depends on firm productivity and size. The job ladder in marginal surplus that emerges in equilibrium determines net poaching patterns by firm characteristics that are in line with the data. As frictions vanish, the model converges to a standard competitive model of firm dynamics. The combination of firm dynamics and search frictions allows the model to: (i) quantify the misallocation cost of frictions; (ii) replicate elusive life‐cycle growth profiles of superstar firms; and (iii) make sense of the failure of the job ladder around the Great Recession as a result of the collapse of firm entry.

We study the random Strotz model, a version of the Strotz (1955) model with uncertainty about the nature of the temptation that will strike. We show that the random Strotz representation is unique and characterize a comparative notion of “more temptation averse.” Also, we demonstrate an unexpected connection between the random Strotz model and a generalization of the Gul–Pesendorfer (GP) (2001) model of temptation which allows for the temptation to be uncertain and which we call random GP. In particular, a preference over menus has a random GP representation if and only if it also has a representation via a random Strotz model with sufficiently smooth uncertainty about the intensity of temptation. We also show that choices of menus combined with choices from menus can distinguish the random GP and random Strotz models.

We consider nonparametric estimation of a regression function that is identified by requiring a specified quantile of the regression “error” conditional on an instrumental variable to be zero. The resulting estimating equation is a nonlinear integral equation of the first kind, which generates an ill-posed inverse problem. The integral operator and distribution of the instrumental variable are unknown and must be estimated nonparametrically. We show that the estimator is mean-square consistent, derive its rate of convergence in probability, and give conditions under which this rate is optimal in a minimax sense. The results of Monte Carlo experiments show that the estimator behaves well in finite samples.