The paper traces Trygve Haavelmo's training and early career as an econometrician from graduation in economics at the University of Oslo in 1933 until his departure for the United States in 1939. The overwhelming influence on Haavelmo in this period was Ragnar Frisch, whose econometric laboratory at the University of Oslo was Haavelmo's workplace and training ground. In the latter part of the period Haavelmo traveled in Europe, mostly within the network of econometricians Frisch had been instrumental in establishing. Haavelmo's work with Frisch, his interaction with other econometricians and statisticians, and his own scholarly work are set out in some detail, allowing assessment to be made of the development of Haavelmo's econometric ideas. Of particular interest is how far his ideas had evolved by 1939. This paper deals with Frisch and his research program in the early 1930s. Haavelmo's activities are narrated by and large chronologically. A sequel to this paper will deal with Haavelmo's scientific activities while in the United States from 1939 to 1944.I have benefited greatly from extensive, generous, and very insightful advice—and prodding—from three anonymous referees on earlier drafts. I am also most grateful for advice and encouragement from the editor. To one of the anonymous referees I also owe the title. I absolve the referees and take full responsibility for all remaining errors and shortcomings. The paper draws on correspondence and documents from the Rockefeller Archive Center, Tarrytown, New York, and the Frisch Correspondence Files at the National Library of Norway and in addition from the Frisch and Haavelmo archives, currently at the Department of Economics, University of Oslo. I am most grateful to professor emeritus Tore S. Thonstad, who has done a great job of organizing the Frisch and Haavelmo archives. I have also benefited from the comprehensive Frisch bibliography prepared by professor emeritus Kåre N. Edvardsen. I thank J.J. Polak for reminiscences, Yngve Willassen for information, E.S. Jansen and J. Kiviet for a push to submit, and Hilde Bojer and Inger Bjerkodden for encouragement.

A vector autoregression with deterministic terms and with no restrictions to its characteristic roots is considered. Strong consistency results for the least squares statistics are presented. This extends earlier results where deterministic terms have not been considered. In addition the convergence rates are improved compared with earlier results.Comments from S. Johansen are gratefully acknowledged.

We consider the statistical properties of the variance ratio statistic in the context of testing for market efficiency defined by uncorrelated returns. The statistic is then the ratio of the variance of K-period returns to the variance of one-period returns scaled by K. We use a continuous-time asymptotic framework whereby we let the sample size increase to infinity keeping the span of the data fixed. We also let the aggregation parameter K increase such that K/T → κ as T, the sample size, increases to infinity. We consider the limit of the statistic under the null hypothesis and under three alternative hypotheses that have been popular in the finance literature. Our analysis permits us to address size and power issues with respect to κ and the sampling interval used. Our theoretical and simulation results show that power is initially increasing as κ increases but then decreases with further increases in κ. This shows that for any given alternative there exists a value of K relative to T that will maximize power. We thus investigate the properties of a test that is the maximal value of the variance ratio over a range of possible values for K. The importance of the trimming to define this range is highlighted.This paper is drawn from chapter 2 of Vodounou's Ph.D. dissertation at the Université de Montréal (Vodounou, 1997). We thank two referees for useful comments and Tomoyoshi Yabu and Xiaokang Zhu for research assistance. Perron acknowledges financial support from the Social Sciences and Humanities Research Council of Canada (SSHRC), the Natural Sciences and Engineering Council of Canada (NSERC), the Fonds pour la Formation de Chercheurs et l'Aide à la Recherche du Québec (FCAR), and the National Science Foundation (grant SES-0078492).

We conduct Monte Carlo simulations to evaluate the use of information criteria (Akaike information criterion [AIC] and Schwarz information criterion [SC]) as an alternative to various probability-based tests for determining cointegrating rank in multivariate analysis. First, information criteria are used to determine cointegrating rank given the lag order in a levels vector autoregression. Second, information criteria are used to determine the lag order and cointegrating rank simultaneously. Results show that AIC has an advantage over trace tests for cointegrated or stationary processes in small samples. AIC does not perform well in large samples. The performance of SC is close to that of the trace test. SC shows better large sample results than AIC and the trace test, even if the series are close to nonstationary series or they contain large negative moving average components. We also find evidence that supports the joint estimation of lag order and cointegrating rank by the SC criterion. We conclude that information criteria can complement traditional parametric tests.We are grateful to Peter C.B. Phillips and an anonymous referee for their comments, which significantly improved the paper.

Jan Kmenta is the author of the internationally respected text The Elements of Econometrics [1] and co-editor of several books related to econometric model building. His published research over 40 years relates to many facets of econometric theory and practice, including the estimation of production functions, the evaluation of structural econometric models, and estimation in the face of missing data. This work has appeared in the leading journals of the profession. A 1966 paper [11] is of historical interest for being the second econometric model constructed of the Australian economy. In his work, through both constructive contributions and methodological critique, he has always sought to highlight and advance the evidently close connection between economics and econometrics.

Kmenta was born on January 3, 1928, in Prague, the Czech Republic. He was educated at the Jirasek State Gymnasium in Prague (1939–47) and the Czech University of Technology in Prague (1947–49). At the University of Sydney (1952–55), Australia, he graduated with a Bachelor of Economics degree (First Class Honors). He obtained a Master of Arts degree (1959) and a Doctor of Philosophy degree (1964) from Stanford University with a doctoral dissertation entitled “Australian Postwar Immigration: An Econometric Study.” After teaching stints in Australia, Stanford, and Wisconsin, he taught at Michigan State from 1965 to 1973 and at the University of Michigan from 1973 to 1993. He is currently Professor Emeritus of Economics and Statistics, University of Michigan, and Visiting Professor, CERGE-EI, Charles University, Prague.

His various academic awards and prizes include the Alexander von Humboldt Foundation Award for Senior U.S. Scientists, the Michigan Economic Society Best Professor Award, the Royal Economic Society Lecturer at the University of Leicester, and the Karel Englis Medal of the Academy of Sciences of the Czech Republic. He was awarded an honorary doctorate from the University of Saarland, Germany, and served as associate editor, Journal of the American Statistical Association (1973–1979 and 1985–1992), associate editor, Review of Economics and Statistics (1975–1992), and associate editor, Metrika (1981–1985). Kmenta was listed as 40th among all economists ranked by the total number of citations (Medoff 1989).

This is an edited transcript of two tape-recorded interviews conducted with Professor Kmenta in Sydney on March 12, 2004, as he visited the University of New South Wales to present the seminar Econometrics: A Failed Science? I am indebted to one of Kmenta's former students, Eric Sowey, for suggesting this project and for his enthusiasm and support throughout.

Teaching graduate econometrics means covering three different kinds of subject matter: a grounding in the theory of econometrics, a long laundry list of available econometric techniques, and an introduction to the fact that the practice of linking models and data is every bit as untidy as mathematical statistics is neat. I assign Econometric Theory and Methods (ETM) as a primary text in our first Ph.D. econometrics course. ETM is in charge of getting the students their theoretical grounding. I also assign Greene's excellent Econometric Analysis (2003) for its coverage of a long list of techniques. My laptop, EViews, and I, together with a whole lot of real data, are responsible for being untidy.

We give a brief introduction to the vector autoregressive model for cointegrated I(2) variables and show how some plausible economic relations can be formulated in the I(2) framework in such a way that likelihood ratio tests for their validity are asymptotically χ2 distributed.The authors thank Paolo Paruolo for helpful comments and the ESF for financial support in the framework of the EMM network.

In this paper we consider the estimation of the expectation vector Xβ under the general linear model {y,Xβ,σ2V}. We introduce a new handy representation for the rank of the difference of the covariance matrices of the ordinary least squares estimator OLSE(Xβ) (= Hy, say) and the best linear unbiased estimator BLUE(Xβ) (= Gy, say). From this formula some well-known conditions for the equality between Hy and Gy follow at once. We recall that the equality between Hy and Gy can be characterized by the rank-subtractivity ordering between the covariance matrices of y and Hy. This rank characterization suggests a particular presentation for the rank of the difference of the covariance matrices of Hy and Gy. We show, however, that this presentation is valid if and only if the model is connected.

Let α* be any basis of col(Π) and β* be any basis of col(Π′); we note col(α⊥) = col(α*⊥), col(β⊥) = col(β*⊥) so no distinction is made between α⊥ and α*⊥, β⊥ and β*⊥ in the following discussion.