Econometric Theory is proud to announce the winning article for the Tjalling C. Koopmans Econometric Theory Prize over the period 1994–1996 (inclusive). The prize is jointly supported by the publishers, Cambridge University Press, and Mrs. Truus Koopmans. It is named in honor of Tjalling C. Koopmans, the 1975 Nobel Laureate in Economic Science. The selection of the winning article was made by the Advisory Board of the Journal, and all articles published in Econometric Theory (1994–1996) were candidates for the prize, except those that were authored or coauthored by the Editor and members of the Advisory Board. The prize is accompanied by a financial award of $500 to each of the winning authors.

Diffuse priors lead to pathological posterior behavior when used in Bayesian analyses of simultaneous equation models (SEM's). This results from the local nonidentification of certain parameters in SEM's. When this a priori known feature is not captured appropriately, it results in an a posteriori favoring of certain specific parameter values that is not the consequence of strong data information but of local nonidentification. We show that a proper consistent Bayesian analysis of a SEM explicitly has to consider the reduced form of the SEM as a standard linear model on which nonlinear (reduced rank) restrictions are imposed, which result from a singular value decomposition. The priors/posteriors of the parameters of the SEM are therefore proportional to the priors/posteriors of the parameters of the linear model under the condition that the restrictions hold. This leads to a framework for constructing priors and posteriors for the parameters of SEM's. The framework is used to construct priors and posteriors for one, two, and three structural equation SEM's. These examples together with a theorem, showing that the reduced forms of SEM's accord with sets of reduced rank restrictions on standard linear models, show how Bayesian analyses of generally specified SEM's can be conducted.

Given an observation of a discrete-time process {Yi,i = 0...n} assumed to be Markov, stationary, and time reversible, we develop a (conservative) test procedure of embeddability by a continuous-time reversible Markov process. The test statistic is derived from a set of moment inequality restrictions implied by the spectral properties of such continuous-time processes. Most interesting is that the embeddability condition of interest is a direct extension of the well-known embeddability problem by a two-state Markov chain. Empirical experiments show that the embeddability hypothesis is rejected more frequently for exchange rate daily data than for stock indices data.

This paper derives the joint moment generating function of quadratic forms occurring in seasonal autoregressive models under stationary, unit root, and explosive specifications. The results are then used to investigate the impact of the seasonal periodicity parameter on various distributional results for both the normalized ordinary least squares coefficient and t-ratio and its effects on the asymptotic bias of parameter estimates.

Efficiency scores of production units are measured by their distance to an estimated production frontier. Nonparametric data envelopment analysis estimators are based on a finite sample of observed production units, and radial distances are considered. We investigate the consistency and the speed of convergence of these estimated efficiency scores (or of the radial distances) in the very general setup of a multi-output and multi-input case. It is shown that the speed of convergence relies on the smoothness of the unknown frontier and on the number of inputs and outputs. Furthermore, one has to distinguish between the output- and the input-oriented cases.

This book is a modern introduction to measure theoretic probability and statistical inference well targeted for graduate students in econometrics at top institutions. It would make an excellent textbook for first year graduate students who intend to specialize in econometrics or who have an advanced mathematical background, and it would also be a useful part of any graduate econometrics course. It is concise and intensely focused on the key conceptual points, thus counteracting the tendency toward long-windedness apparent in some recent econometric texts. Nevertheless, it provides many valuable insights into difficult material. In particular, the discussions of sigma fields and conditional expectation given a sigma field are very helpful. The coverage of multivariate concepts alongside univariate ones is particularly useful to econometricians and something that is missing from most comparable statistical texts. The author has a mature attitude to proof, providing complete and illuminating proofs of some results but making liberal use of simplifications provided by special cases, for example in Theorems 4.1 and 4.5 and Section 5.2.2, to shorten and focus the arguments. The proofs themselves are very clear and well presented. Carefully chosen diagrams are given throughout the book that nicely illustrate many of the key concepts. In addition, each chapter contains a long list of problems of varying complexity, which will be useful to instructors.

The Econometric Society Australasian Meeting in 1997 was hosted by the economics department at the University of Melbourne on July 2 to 4 with Peter Bardsley (economics) and Vance L. Martin (econometrics) as cochairpersons. Special features consisted of a star-studded lineup of invited speakers, the inaugural E.J. Hannan lecture, a range of special seminars in economic theory and econometric theory, and a number of nonacademic but spirited special events that took place both during and after the conference.