This paper updates rankings of academic institutions by Hall (1990, Econometric Theory 6, 1–16) and Baltagi (1998, Econometric Theory 14, 1–43) by publication activity in econometrics over the period 1989–1999. This is based on 15 leading international journals that publish econometrics articles. In addition, a list of the top 150 individual producers of econometrics in these 15 journals over this period is provided. This is done for theoretical econometrics and also for all contributions in econometrics. Sensitivity analysis is provided using (i) alternative conversion factors given to the 15 journals based on citation impact factors, (ii) alternative ranking using the number of articles published in these journals, (iii) separate rankings for both institutions and individuals by journal, (iv) rankings for institutions and individuals based on publications in three core journals.I thank Peter C.B. Phillips for entrusting me with this study, three referees and the editor for their comments and suggestions, and P.M. Kisor for research assistance.

A common test in econometrics is the Dickey–Fuller test, which is based on the test statistic . We investigate the behavior of the test statistic if the data yt are given by an exponential random walk exp(Zt) where Zt = Zt−1 + σεt and the εt are independent and identically distributed random variables. The test statistic DF(T) is a nonlinear transformation of the partial sums of εt process. Under certain moment conditions on the εt we show that tends to one as λ → 0. For the particular case that the εt define a simple random walk it is shown that plimT→∞DF(T)/T exists and the limit is evaluated. The theoretical results are illustrated by some simulation experiments.We gratefully acknowledge the help of an anonymous referee whose comments on the first two versions of this paper enabled us to reduce the number of mistakes and to increase the clarity of presentation. The authors' research was supported in part by Sonderforschungsbereich 475, University of Dortmund.

A concise derivation of the Wallace and Hussain fixed effects transformation.

We propose a new diagnostic test for linear and nonlinear time series models, using a generalized spectral approach. Under a wide class of time series models that includes autoregressive conditional heteroskedasticity (ARCH) and autoregressive conditional duration (ACD) models, the proposed test enjoys the appealing “nuisance-parameter-free” property in the sense that model parameter estimation uncertainty has no impact on the limit distribution of the test statistic. It is consistent against any type of pairwise serial dependence in the model standardized residuals and allows the choice of a proper lag order via data-driven methods. Moreover, the new test is asymptotically more efficient than the correlation integral–based test of Brock, Hsieh, and LeBaron (1991, Nonlinear Dynamics, Chaos, and Instability: Statistical Theory and Economic Evidence) and Brock, Dechert, Scheinkman, and LeBaron (1996, Econometric Reviews 15, 197–235), the well-known BDS test, against a class of plausible local alternatives (not including ARCH). A simulation study compares the finite-sample performance of the proposed test and the tests of BDS, Box and Pierce (1970, Journal of the American Statistical Association 65, 1509–1527), Ljung and Box (1978, Biometrika 65, 297–303), McLeod and Li (1983, Journal of Time Series Analysis 4, 269–273), and Li and Mak (1994, Journal of Time Series Analysis 15, 627–636). The new test has good power against a wide variety of stochastic and chaotic alternatives to the null models for conditional mean and conditional variance. It can play a valuable role in evaluating adequacy of linear and nonlinear time series models. An empirical application to the daily S&P 500 price index highlights the merits of our approach.We thank the co-editor (Don Andrews) and two referees for careful and constructive comments that have lead to significant improvement over an earlier version. We also thank C.W.J. Granger, D. Tjøstheim, and Z. Xiao for helpful comments. Hong's participation is supported by the National Science Foundation via NSF grant SES–0111769. Lee thanks the UCR Academic Senate for research support.

Louis (1982) presents a method for computing the observed information matrix and standard errors of maximum likelihood estimates obtained via the EM algorithm based on the complete-data log likelihood function. The problem illustrates the well-known method of Louis (1982) for a widely used qualitative response model in econometrics. The observed-data log likelihood function for the following model can, of course, be easily differentiated to obtain the observed information matrix; our objective is to illustrate the method and not to recommend its use for this model.

Denis Sargan's intellectual influence in econometrics is discussed and some of his visions for the future of econometrics are considered in this memorial article. One of Sargan's favorite topics in econometric theory was finite sample theory, including both exact theory and various types of asymptotic expansions. We provide some summary discussion of asymptotic expansions of the type that Sargan developed in this field and give explicit representations of Sargan's formula for the Edgeworth expansion in the case of an econometric estimator that can be written as a smooth function of sample moments whose distributions themselves have Edgeworth expansions.Parts of Section 1 were presented in March 2002 in the author's Sargan Lecture at the Royal Economics Society Conference, Warwick, UK. My thanks go to John Chao, David Hendry, Essie Maasoumi, Peter Robinson, and Katsumi Shimotsu for helpful comments on the original version of this paper. I learned the Chinese saying that heads this article from Sainan Jin. Thanks also go to the NSF for research support under grant SES 00-92509.

A student is like green grass and a great teacher is like the spring sun. The benefit from the sun is infinite, and little grass can hardly pay it back, although it tries its best.

—Chinese saying

Consider the regression , where and the exact functional form of f is unknown, although we do know that f is homogeneous of known degree r. Using a local linear approach, we examine two ways of nonparametrically estimating f: (i) a “direct” approach and (ii) a “projection based” approach. We show that depending upon the nature of the conditional variance , one approach may be asymptotically better than the other. Results of a small simulation experiment are presented to support our findings.We thank Don Andrews and an anonymous referee for comments that greatly improved this paper. The first author thanks Professor Wolfgang Härdle for hospitality at the Institute of Statistics and Econometrics, Humboldt University, Berlin, where part of this research was carried out. Financial support to the first author from Sonderforschungsbereich 373 (“Quantifikation und Simulation Ökonomischer Prozesse”) and the NSF via grants SES-0111917 and SES-0214081 is also gratefully acknowledged.

Over the past decade financial econometrics has evolved from a small subfield of econometrics to a rapidly growing area of research that maintains its own journals and dominates the applied time series sessions in many major conferences. Financial econometrics is a conglomerate of statistical models and techniques to summarize regular features of financial data sets and to evaluate theoretical models and hypotheses that have been set forth by financial economists. Besides being a focal point of intellectual curiosity, financial econometrics also provides a rich tool kit that is used by practitioners around the world to forecast volatilities of asset return, optimize portfolio allocations, price derivatives, and manage market risk. Courses in financial econometrics are being offered at all major universities and business schools at the master's and doctoral levels. Given the importance and popularity of the field, it is not surprising that it has become the subject of textbooks and monographs, one of them being the new book Financial Econometrics by Christian Gourieroux and Joann Jasiak.This review has benefited from discussions with Frank Diebold and participants of the Penn Econometrics Lunch.

Consistent standard errors for target variance approach to GARCH estimation.

Professor Davidson's Econometric Theory offers a clear, well written, graduate level econometrics textbook, which would be highly appropriate as the principal text for the second and/or third semester of a general Ph.D. econometrics sequence or for a specialized course on time series. It provides good reading material for anyone interested in deepening his or her understanding of econometrics and also a valuable reference source for econometricians. Although the book reviews the basics of multivariate regression, it is written at a level that requires either some degree of mathematical sophistication or prior familiarity with econometrics. As a result, it may be too advanced for a first semester Ph.D. course, except where students have strong preparation.

Fixed effects estimation of the population-averaged slopes in a panel data random coefficient model.

Let and Θ be infinite sets and let × Θ. We show that the class of projections of A onto is a Vapnik–Chervonenkis (VC) class of sets if and only if the class of projections of A onto Θ is a VC class. We illustrate the result in the context of semiparametric estimation of a transformation model. In this application, the VC property is hard to establish for the projection class of interest but easy to establish for the other projection class.

Consider the following stationary time series regression.

The p values of structural break tests, when the break date or dates are unknown, must be calculated in terms of the probability distributions of functions of Bessel processes. The literature so far has maintained that direct computation of these p values and of the corresponding critical values is too difficult and has relied on approximations based on simulations, asymptotic expansions, or curve fitting. This paper presents a fast simple method of calculating exact p values and critical values and uses the method to evaluate the accuracy of the various approximations.The author is grateful for comments and suggestions from Don Andrews, Clint Cummins, Jeff Fuhrer, Ken Garbade, Jim Mahoney, Tony Rodrigues, Josh Rosenberg, Sebastian Schich, participants in a workshop at the Federal Reserve Bank of New York, and the referees. The views expressed in this paper are those of the author and do not necessarily represent those of the Federal Reserve Bank of New York or the Federal Reserve System.

Let Z be an n × (n − q) matrix of rank n − q whose columns span the space orthogonal to the space spanned by the columns of X and let f = Z′y. Then, on premultiplying by the n × n nonsingular matrix [Z : X], we find that the conventional least squares problem.

A Mixingale inequality using an exponential moment.

ARMA representation of squared Markov switching heteroskedastic models—solution.

This book seeks to integrate research on cause and effect inference from cognitive science, econometrics, epidemiology, philosophy, and statistics. It puts forward the work of its author, his collaborators, and others over the past two decades as a new account of cause and effect inference that can aid practical researchers in many fields, including econometrics. Pearl adheres to several propositions on cause and effect inference. Though cause and effect relations are fundamentally deterministic (he explicitly excludes quantum mechanical phenomena from his concept of cause and effect), cause and effect analysis involves probability language. Probability language helps to convey uncertainty about cause and effect relations but is insufficient to fully express those relations. In addition to conditional probabilities of events, cause and effect analysis requires graphs or diagrams and a language that distinguishes intervention or manipulation from observation. Cause and effect analysis also requires counterfactual reasoning and causal assumptions in addition to observations and statistical assumptions.

It is easy to see that Problem 02.1.2 holds more generally in that the result is valid for B Hermitian complex (rather than “symmetric real”), for m = 0,1,…,q (rather than just for “m < q”), for r ≠ 0 (rather than just for “integer r > 1”), and for tr(Bk) = tr(Ck), k = 1,2,3,4 (rather than for “k = 1,2,…”), as shown by Shanbhag (1970).