The Tjalling C. Koopmans Econometric Theory Prize is named in honor of Tjalling C. Koopmans, the 1975 Nobel Laureate in economic science, whose contributions to the development of econometrics are of fundamental and lasting importance to the subject. The prize is awarded once every three years for the best article reporting original research published in the Journal Econometric Theory over that period. The selection of the winning article is made by the Advisory Board of the Journal and the criteria for selection is based on Tjalling Koopmans’ own research which is universally admired for its rigor, clarity and originality. All articles published in Econometric Theory are candidates for the prize except those that are authored or co-authored by the Editor and members of the Advisory Board.

The prize is accompanied by a financial award of $1,000. It is supported by the publishers, Cambridge University Press, and Mrs. Truus Koopmans.

Cambridge University Press joins me in congratulating the authors on their success in receiving this award.

2006–2008

Wei Biao Wu and Xiaofeng Shao, "A Limit Theorem for Quadratic Forms and its Applications," Econometric Theory, Vol. 23, No. 5, October 2007, pages 930-951.

The paper derives a central limit theorem for quadratic forms of martingale differences. Particular emphasis is laid on the application of this result to estimation of the spectral density of a stationary process by the smoothed periodogram. For this case asymptotic normality is obtained from the result on general quadratic forms by approximating the Fourier transforms of the underlying stationary process by martingales. Such limiting results are important, for instance for hypothesis testing and construction of confidence intervals in frequency domain.

 For both, the general case and for the special case of estimation of spectra, there exists a substantial body of preceding literature. A special feature of this paper is that the results are derived under assumptions which are very general and easily verifiable. For spectral estimation the main assumptions are that the underlying stationary process is obtained from a – in general non-linear – causal transformation of an i.i.d. sequence and a very weak assumption of short range dependence. By the first assumption the stationary process can be interpreted as the output of a general, possibly nonlinear, system with iid inputs. The class of such processes is very large. The second assumption avoids the classical strong mixing conditions or summability conditions on the joint cumulants.