This paper is concerned with estimating a conditional quantile function that is assumed to be partially linear. The paper develops a simple estimator of the parametric component of the conditional quantile. The semiparametric efficiency bound for the parametric component is derived, and two types of efficient estimators are considered. Asymptotic properties of the proposed estimators are established under regularity conditions. Some Monte Carlo experiments indicate that the proposed estimators perform well in small samples.This paper is a part of my Ph.D. dissertation submitted to the University of Iowa. I am grateful to my adviser, Joel Horowitz, for his insightful comments, suggestions, guidance, and support. I also thank John Geweke, Gene Savin, two anonymous referees, the co-editor Oliver Linton, and participants at the 2001 Midwest Econometrics Group Annual Meeting in Kansas City for many helpful comments and suggestions. Of course, the responsibility for any errors is mine.

In this note we consider the asymptotic power functions of some bootstrap unit root tests under local alternatives and show that they are in fact the same as for ordinary unit root tests. This is regardless of whether the differences of the observations, i.e., the so-called restricted residuals, or the ordinary least squares residuals are used to construct the resampled observations. We also consider models containing a constant and a linear trend and the DF-GLS tests proposed by Elliott, Rothenberg, and Stock (1996, Econometrica 64, 813–836). A small Monte Carlo experiment is included.I thank the associate editor, Bruce E. Hansen, and three anonymous referees for very constructive comments on the previous versions of the manuscript.

This paper examines the effects of temporal aggregation on the asymptotic variances of estimators in cointegrated systems. Two important findings are obtained. First, estimators based on flow data alone are more efficient than when the data are all stocks or a mixture of stocks and flows. Second, estimators based on flow data are as efficient as when the data are recorded continuously. A method of improving efficiency with stock variables is also proposed, and an empirical illustration of the method is provided in the context of long-run money demand regressions.I thank Roy Bailey, Rex Bergstrom, Roderick McCrorie, a co-editor, and two anonymous referees for helpful comments. I also thank Katsumi Shimotsu for help with some data issues. None of these individuals are implicated, however, in any possible shortcomings of this paper. The financial support provided by the ESRC under grant R000221818 is gratefully acknowledged.

It is possible to view econometric history as the retrospective assessment of intellectual resource allocations. This paper uses such a perspective to examine the history of extraneous estimation. Independently proposed by Marschak (1939, Econometrica 7, 332–335) and Wold (1940, Efterfrågan på jordbruksprodukter och dess känslighet för prisoch inkomstförändringar (The demand for agricultural products and its sensitivity to price and income changes)), extraneous estimation was widely used as a collinearity solution in demand analysis from 1950 to 1980. We give a detailed account of the origins of this procedure and the theoretical work that clarified the statistical structure that underpinned it. The subsequent literature is then assessed in terms of the optimal use of available econometric tools and the interaction between theory and applications. We find that optimal econometric procedures were rarely used and that theoretical refinements continued to be published well after extraneous estimation had ceased to be a useful tool for demand analysis.An earlier version of this paper titled “To Pool or Not to Pool? Why Is That Still a Question?” was presented at the Thirteenth Annual Meeting of the Canadian Econometrics Study Group, University of Waterloo, September 21–22, 1996. I thank Robin Carter for his comments at that presentation. Another version titled “The Optimum Amount of Econometrics” was presented on October 31, 1998, at Concordia University on the occasion of Gordon Fisher's seventieth birthday. I am indebted to Lady Giovanna Stone and Peter Jones of King's College Library for arranging access to Sir Richard Stone's papers and to the staff of the UCLA Research Library for access to the Jacob Marschak Archive. James Tobin was kind enough to share some of his recollections of the “extraneous estimation” period. George Szava-Kovats and Chris Minns provided able research assistance, and Charlene Hill typed the manuscript with her customary efficiency. Colin and Sally Ash, Reinhold Bergstrom, Jim Hamilton, Balder von Hohenbalken, Joe Ostroy, and Martin Weale provided help of the sort that does not lend itself to detailed elaboration. An earlier version of the paper had the benefit of a close and perceptive reading by John Aldrich. The referees' and editor's comments have also helped shape the paper into its present form, and I thank them for their contributions.

In Pötscher (1991, Econometric Theory 7, 163–185) the asymptotic distribution of a post-model-selection estimator, both unconditional and conditional on selecting a correct model (minimal or not), has been derived. Limitations of these results are (i) that they do not provide information on the distribution of the post-model-selection estimator conditional on selecting an incorrect model and (ii) that the quality of this asymptotic approximation to the finite-sample distribution is not uniform with respect to the underlying parameters. In the present paper we first obtain the unconditional and also the conditional finite-sample distribution of the post-model-selection estimator, which turn out to be complicated and difficult to interpret. Second, we obtain approximations to the finite-sample distributions that are as simple and easy to interpret as the asymptotic distributions obtained in Pötscher (1991) but at the same time are close to the finite-sample distributions uniformly with respect to the underlying parameters. As a by-product, we also obtain the asymptotic distribution conditional on selecting an incorrect model.We thank the co-editor Richard Smith and the two referees for helpful comments on a previous version of this paper. Hannes Leeb's research was supported by the Austrian Science Foundation (FWF), project P13868-MAT.

In this paper, functional limit theorems for general fractional processes are established under quite weak conditions. The results are then used to derive weak convergence of general nonstationary fractionally integrated processes and to characterize unit root distribution in a model with error being a fractional autoregressive moving average process or a nonstationary fractionally integrated process.The authors thank three referees and an associate editor for their detailed reading of this paper and valuable comments, which have led to this much improved version of the paper.

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.

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.

Consider the following stationary time series regression.

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.

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).