We construct properly scaled functions of Rp-valued partial sums of demeaned data and derive bounds via the functional law of the iterated logarithm for strong mixing processes. If we obtain a value below or equal to the bound we decide in favor of I(0); otherwise we decide in favor of I(1). This provides a consistent rule for classifying time series as being I(1) or I(0). The nice feature of the procedure lies in the almost sure nature of the bound, guaranteeing a lim sup–type result. We finally provide conditions for the strong consistency of estimators of the variance in the dependent and heterogeneous case.

This paper considers cointegrating regressions with time varying coefficients. The coefficients are modeled as smooth functions evolving over time. It is shown that they can be estimated nonparametrically, using suitably modified series estimators. Presented is the efficient method of estimation, which relies on simple prefiltering of the data and preestimation of the model. The test for the adequacy of model specification is also developed. Our model and statistical methods are applied to analyze the U.S. automobile demand function.

It is possible to construct a test of the null of no fractional integration that has nontrivial asymptotic power against a sequence of alternatives specifying that the series is I(d) with d = O(T−1/2), where T is the sample size. In this paper, I show that tests for fractional integration that are based on the partial sum process of the time series have only trivial asymptotic power (i.e., equal to the size) against this sequence of local alternatives. These tests include the rescaled-range test. In this sense, despite its widespread use in empirical work, the rescaled-range test is a poor test for fractional integration.

This paper proposes a version of the integrated conditional moment (ICM) test that is optimal for a class of composite alternatives. The ICM test is built on the fact that a random function based on a correctly specified model should have zero mean, whereas any misspecification in the conditional mean implies a divergent mean for the random function. We derive test statistics that are optimal for each basis element of an orthonormal decomposition of the function space for which the random function is an element. We then use a weighted summation of these test statistics to compose the single test statistic that is optimal for any pair of alternatives that are symmetric about zero. This test is equivalent to using a particular measure in the ICM test of Bierens and Ploberger (1997, Econometrica 65, 1129–1152).

We rank institutions and researchers based on a standardized page count of their econometric theory publications over the last 11 years (1986–1996) in 11 economics and statistics journals. Our ranking criteria differ from those employed by Hall (1987, Econometric Theory 3, 171–194; 1990, Econometric Theory 6, 1–16) and Baltagi (1998, Econometric Theory 14, 1–43). We weight the standardized page count of a publication by the publishing journal's “impact factor,” which measures a journal's impact on the profession. We also depart from the previous rankings by focusing only on publications in theoretical econometrics. Our rankings reveal Yale University to be the leading academic institution, enjoying a large lead over the other top institutions: University of Chicago, M.I.T., and London School of Economics. Our rankings also reveal that Peter Phillips and Donald Andrews (both affiliated with Yale University) are the leading researchers in theoretical econometrics. We also provide rankings of countries and Ph.D. programs.

Over the last three decades, G.S. Maddala (universally known as “G.S.”) has been a familiar name to all doing econometric work. His substantial contributions to the discipline through numerous books and articles single him out as one of the most distinguished econometricians of our time. Because of his extraordinary ability to synthesize and exposit complex methodological results in simple intuitive terms, he has influenced econometric research in other areas of social science also. According to the Social Science Citation Index, G.S. has been one of the top five most cited econometricians during each of the years 1988–1993. He writes econometrics in plain English with a characteristic sense of wit and humor. There cannot be too many empirical economists around the world who have not been influenced by G.S.'s writings in some way or other. Often he has taken a critical look at evolving econometric techniques, particularly those that have no practical applications, and has not hesitated to go against the tide of the profession.