Under general conditions the sample covariance matrix of a vector martingale and its differences converges weakly to the matrix stochastic integral ∫01BdB′, where B is vector Brownian motion. For strictly stationary and ergodic sequences, rather than martingale differences, a similar result obtains. In this case, the limit is ∫01BdB′ + Λ and involves a constant matrix Λ of bias terms whose magnitude depends on the serial correlation properties of the sequence. This note gives a simple proof of the result using martingale approximations.

This paper studies a class of models where full identification is not necessarily assumed. We term such models partially identified. It is argued that partially identified systems are of practical importance since empirical investigators frequently proceed under conditions that are best described as apparent identification. One objective of the paper is to explore the properties of conventional statistical procedures in the context of identification failure. Our analysis concentrates on two major types of partially identified model: the classic simultaneous equations model under rank condition failures; and time series spurious regressions. Both types serve to illustrate the extensions that are needed to conventional asymptotic theory if the theory is to accommodate partially identified systems. In many of the cases studied, the limit distributions fall within the class of compound normal distributions. They are simply represented as covariance matrix or scalar mixtures of normals. This includes time series spurious regressions, where representations in terms of functionals of vector Brownian motion are more conventional in recent research following earlier work by the author.

Nine leading journals that publish statistical theory are used to provide a data base of institutional and individual research activity in statistics over the period 1980–1986. From this data base, we construct both institutional and individual research rankings according to standardized page counts of articles published in these journals over the stated period. The study is worldwide and we provide breakdowns of publication by country and by journal. Separate rankings are also provided for both institutions and individuals according to publication track records in the Annals of Statistics alone.

In [4] Chan and Tran give the limit theory for the least-squares coefficient in a random walk with i.i.d. (identically and independently distributed) errors that are in the domain of attraction of a stable law. This paper discusses their results and provides generalizations to the case of I(1) processes with weakly dependent errors whose distributions are in the domain of attraction of a stable law. General unit root tests are also studied. It is shown that the semiparametric corrections suggested by the author in other work [22] for the finite-variance case continue to work when the errors have infinite variance. Surprisingly, no modifications to the formulas given in [22] are required. The limit laws are expressed in terms of ratios of quadratic functional of a stable process rather than Brownian motion. The correction terms that eliminate nuisance parameter dependencies are random in the limit and involve multiple stochastic integrals that may be written in terms of the quadratic variation of the limiting stable process. Some extensions of these results to models with drifts and time trends are also indicated.

Using generalized functions of random variables and generalized Taylor series expansions, we provide quick demonstrations of the asymptotic theory for the LAD estimator in a regression model setting. The approach is justified by the smoothing that is delivered in the limit by the asymptotics, whereby the generalized functions are forced to appear as linear functionals wherein they become real valued. Models with fixed and random regressors, and autoregressions with infinite variance errors are studied. Some new analytic results are obtained including an asymptotic expansion of the distribution of the LAD estimator.

We are pleased to announce award of the A.R. Bergstrom Prize in Econometrics for 2007 to Melanie Morten, Ph.D. student, Yale University, for her paper, “Healthy and Wealthy? Examining the Causality between Income and Life Expectancy.”

This note analyzes the local asymptotic power properties of a test proposed by Breitung (2000, in B. Baltagi (ed.), Nonstationary Panels, Panel Cointegration, and Dynamic Panels). We demonstrate that the Breitung test, like many other tests (including point optimal tests) for panel unit roots in the presence of incidental trends, has nontrivial power in neighborhoods that shrink toward the null hypothesis at the rate of n−1/4T−1 where n and T are the cross-section and time-series dimensions, respectively. This rate is slower than the n−1/2T−1 rate claimed by Breitung. Simulation evidence documents the usefulness of the asymptotic approximations given here.The authors thank Paolo Paruolo and a referee for comments on an earlier version of the paper. Phillips acknowledges partial support from a Kelly Fellowship and the NSF under grant SES 04-142254. Perron acknowledges financial support from FQRSC, SSHRC, and MITACS.

We are pleased to announce two awards for the A.R. Bergstrom Prize in Econometrics for 2005.

We are pleased to announce award of the A.R. Bergstrom Prize in Econometrics for 2003. The award is to Dr. Chirok Han, School of Economics and Finance, Victoria University of Wellington, for his paper, “The Bias of Fixed Effects Estimators for Panel Binary Choice Models.”