The range of correlation coefficient of any bivariate discrete random vector with finite or countably infinite values is derived. We show analytically that the normal-transformed discrete bivariate probability system of Van Ophem (1999, Econometric Theory 15, 228–237) has a flexible correlation coefficient. We establish the strictly monotonic relation of its correlation coefficient with the bivariate normal correlation-coefficient parameter in the system.

We obtain an inequality for the sample variance of a vector Brownian motion on [0,1] and an associated Ornstein–Uhlenbeck process. The result is applied to a regression involving near-integrated regressors, and establishes that in the limit the dispersion of the least squares estimator is greater in the near-integrated than in the integrated case. Our proof uses a quite general integral inequality, which appears to be new.

The York Annual Meetings in Econometrics started in 2000. The first meeting was cosponsored by the University of York and the Royal Economic Society and featured invited talks by Grant Hillier (Southampton), Peter Phillips (Yale, Auckland, and York), and Richard Smith (Bristol). This year's program, which is appended below, featured more invited talks and introduced a poster session for Ph.D. students from the U.K. and beyond. The University of York and Cambridge University Press jointly sponsored this year's event and about 90 people attended. In both years, the keynote papers attracted much interest and audience interaction in lively discussions. We hope that the rising trend in the numbers of papers and the conference participants continues into the future.

We prove in this paper the validity of an Edgeworth expansion to the joint distribution of the sample autocorrelations of a stationary Gaussian long memory process. The method of proof relies on a verification of the suitably modified conditions for the validity of a multivariate Edgeworth expansion of Durbin (1980, Biometrika 67, 311–333). A simulation study proves the expansion to be useful and accurate.

In Econometric Theory, volume 17, number 1, an excellent solution to problem 00.1.3 also had been submitted by S. Puntanen, G.P.H. Styan, and H.J. Werner, and this fact was inadvertently omitted. In the same issue, in the restatement of problem 00.1.1, the phrase “skew symmetric” should read “real skew symmetric”; this modification was suggested by S. Puntanen, G.P.H. Styan, and H.J. Werner.

A corrigendum to Problem 01.2.2 is also described.

In this paper the authors extend results by Harvey and Zhou (1990, Journal of Financial Econometrics 26, 221–254) and Kandel, McCulloch, and Stambaugh (1995, Review of Financial Studies 8(1), 1–53) to derive the posterior distribution of a key parameter in a Bayesian analysis of asset pricing models. It is shown that this distribution depends upon the same terms that constitute the standard asset pricing test of Jobson and Korkie (1985, Canadian Journal of Administrative Science 12, 114–138). Contrary to the view held by other authors, we find straightforward expressions for the posterior distribution that can be calculated without resorting to Monte Carlo methods.