When is the Pareto optimal amount of public goods independent of income distribution? Subject to some regularity conditions, the answer is when preferences of every individual i can be represented by a utility function of the form U(X_i,Y)=A(Y)X_i+B_i(Y) where X_i is i's consumption of private goods and Y is the amount of public goods.

This article proposes a new approach to small sample theory that achieves a meaningful integration of earlier directions of research in this field. The approach centers on the constructive technique of approximating distributions developed recently by the author in [10]. This technique utilizes extended rational approximants (ERA's) which build on the strengths of alternative, less flexible approximation methods (such as those based on asymptotic expansions) and which simultaneously blend information from diverse analytic, numerical and experimental sources. The first part of the article explores the general theory of approximation of continuous probability distributions by means of ERA's. Existence, characterization, error bound, and uniqueness theorems for these approximants are given and a new proof is provided for the convergence result obtained earlier in [10]. Some further aspects of finding ERA's by modifications to multiple-point Pade approximants are presented and the new approach is applied to the noncircular serial correlation coefficient. The results of this application demonstrate how ERA's provide systematic improvements over Edgeworth and saddlepoint techniques. These results, taken with those of the earlier article [10], suggest that the approach offers considerable potential for empirical application in terms of its reliability, convenience, and generality.

[In most statistical estimation problems, the distribution of errors is unknown, and the traditional assumption of normality is used for convenience. We investigate here the fragility of the inferences based on normality by hypothesizing a neighborhood of distributions around the normal distribution, and by identifying the set of alternative maximum likelihood estimates corresponding to the set of error distributions.]

[Forward and spot exchange rates are modelled as an unrestricted bivariate autoregression from weekly data on the New York foreign exchange market for June, 1973 to April, 1980. The null hypothesis that the forward exchange rate is an unbiased estimate of the corresponding future spot exchange rate is tested by means of a nonlinear Wald test and is rejected for all six currencies considered. The results cast doubt on a central assumption in many current models of exchange rate behavior.]

A solution method and an estimation method for nonlinear rational expectations models are presented in this paper.The solution method can be used in forecasting and policy applications and can handle models with serial correlation and multiple viewpoint dates.When applied to linear models, the solution method yields the same results as those obtained from currently available methods that are designed specifically for linear models.It is, however, more flexible and general than these methods.The estimation method is based on the maximum likelihood principal.It is, as far as we know, the only method available for obtaining maximum likelihood estimates for nonlinear rational expectations models.The method has the advantage of being applicable to a wide range of models, including, as a special case, linear models.The method can also handle different assumptions about the expectations of the exogenous variables, something which is not true of currently available approaches to linear models.