This series consists of a number of hitherto unpublished studies, which are introduced by the editors in the belief that they represent fresh contributions to economic science. The term economic analysis as used in the title of the series has been

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.

This paper uses world running records by age to estimate a biological frontier of decline rates. Two models are compared: a linear/ quadratic (LQ) model and a nonparametric model. Two estimation methods are used: (a) minimizing the squared difference between the observed records and the modeled biological frontier and (b) using extreme value theory to estimate the biological frontier that maximizes the probability of observing the existing world records by age. The results support the LQ model and suggest a linear percentage decline up to the late 70s and quadratic decline after that.

Was the Depression forecastable? After the crash, how long should it have taken contempo rary forecasters to realize how severe the downturn was going to be? These questions are addressed by studying the predictions of the Harv ard Economic Service and Yale's Irving Fisher during 1929 and the ear ly 1930s. The data assembled by the Harvard and Yale forecasters, tog ether with modern historical data, are subjected to statistical analy sis to learn whether their verbal pronouncements were consistent with the data. Both the Harvard and Yale forecasters were systematically too optimistic. Yet, nothing in the data suggests that the optimism w as unwarranted. Copyright 1988 by American Economic Association.