We provide rankings of academic institutions according to the publication record of their faculty members in the specialization of econometrics. The rankings are based on standardized page counts of articles published by faculty members in 14 journals over the period 1980 to 1985. Separate rankings of the 200 leading institutions are provided for their theoretical contributions, as well as their contributions in theory and applied work. The rankings are compared with a ranking in the general field of economics.

The invariant polynomials (Davis [8] and Chikuse [2] with r(r ≥ 2) symmetric matrix arguments have been defined, extending the zonal polynomials, and applied in multivariate distribution theory. The usefulness of the polynomials has attracted the attention of econometricians, and some recent papers have applied the methods to distribution theory in econometrics (e.g., Hillier [14] and Phillips [22]).

The ‘top order’ invariant polynomials , in which each of the partitions of ki 1 = 1,…,r, and has only one part, occur frequently in multivariate distribution theory (e.g., Hillier and Satchell [17] and Phillips [27]). In this paper we give three methods of constructing these polynomials, extending those of Ruben [28] for the top order zonal polynomials. The first two methods yield explicit formulae for the polynomials and then we give a recurrence procedure. It is shown that some of the expansions presented in Chikuse and Davis [4] are simplified for the top order invariant polynomials. A brief discussion is given on the ‘lowest order’ invariant polynomials.

Many observed macrovariables are simple aggregates over a large number of microunits. It is pointed out that the generating process of the macrovariables is largely determined by the common factors in the generating mechanisms of the microvariables, even though these factors may be very unimportant at the microlevel. It follows that macrorelationships are simpler than the complete microrelationships, but that empirical investigations of microrelationships may not catch those components, containing common factors, which will determine the macrorelationship. It is also shown that an aggregate expectation or forecast is simply the common factor component of the individual agents expectations.

In this paper we develop full information methods for estimating the parameters of a system of simultaneous equations with error component structure and establish relationships between the various structural estimators.

A Taylor series approach is used to derive approximations to the approximate slope of LR, W, and LM test statistics. These can be useful when the approximate slopes themselves are complicated. The results are applied to the test of linear coefficient restrictions in the linear model with a nonscalar covariance matrix. They can be used to find cases where the Wald test may badly over-reject in this model. The results shed some new light on the over-rejection that has been noted in the special cases of AR(1) disturbances and the SURE model.

Econometrics owes its essence and much of its ongoing vitality to the infusion of ideas from economic theory, the development of appropriate statistical methods, and the quantitative recording of economic phenomena. Each of these elements is an important source of energy for further scientific change. As professionals, we contribute to this evolution through research and teaching, most commonly in individual specialisms and all too frequently with little concern for holistic issues. Few economists indeed have the knowledge, the scientific expertise, and the professional experience to speak out with authority on the subject in its entirety and its scientific achievements. Even fewer command the respect of colleagues and authorities in neighboring disciplines, like mathematical statistics. For some time now, Edmond Malinvaud has stood out from the rest of our profession as a scholar who is uniquely qualified in this regard. His writings influence every field of economics. His advanced textbooks are cornerstones of graduate teaching programs. The scientific standards of his work set an example to the entire profession. And his recent evaluations of scientific accomplishments in quantitative economics have brought unity and direction to what was earlier just uncoordinated, individual research.

In this paper we identify a restriction on the finite-sample application of Magdalinos's asymptotic expansions for the t statistic based on the k-class instrumental variable estimator.

We derive exact finite-sample expressions for the biases and risks of several common pretest estimators of the scale parameter in the linear regression model. These estimators are associated with least squares, maximum likelihood and minimum mean squared error component estimators. Of these three criteria, the last is found to be superior (in terms of risk under quadratic loss) when pretesting in typical situations.