Tjalling C. Koopmans Econometric Theory Prize Awarded
Econometric Theory has announced the winning article for “The Tjalling C. Koopmans Econometric Theory Prize” in its August 2018 issue. The winning article, “Speciﬁcation Tests for Lattice Processes,” was written by Javier Hidalgo (London School of Economics) and Myung Hwan Seo (Seoul National University), and appeard in the April 2015 issue of Econometric Theory (volume 31(2), pages 294–336).
Along with Cambridge University Press, Yale Sterling Professor of Economics and Statistics, and Cowles Research Staﬀ Member, Peter C.B. Phillips, congratulate the authors on their success in receiving the award.
The prize is kindly supported by the Cowles Foundation, Yale University. It is named in honor of Tjalling C. Koopmans, the 1975 Nobel Laureate in Economic Science. The selection of the winning article was made by the Advisory Board of the Journal and all articles published in Econometric Theory over 2015–2017 inclusive were candidates for the prize, except those that were authored or coauthored by the Editor and members of the Advisory Board. The prize is accompanied by a ﬁnancial award of $1000 to the winning author(s).
The citation of the winning paper (written by the Advisory Board and Editor) is as follows:
The article explores the properties of weakly stationary (scalar) random sequences x(t) in which the parameter (t) may be d dimensional on a lattice. Important examples are, for d = 1, noncausal linear dynamical systems and, for d > 1, spatial and spatiotemporal systems, which play a major role in many scientiﬁc areas. Many relevant examples of such processes arise in environmental and development economics. Within this wide context, x(t) is assumed to be a generalized “spatial” linear process, with unobserved “spatial” i.i.d. inputs. This formulation leads to a transfer function in d variables and a spectral density in d variables. The article contributes by developing statistical tests for correct speciﬁcation of the dynamics of x(t), or more precisely, tests of the hypothesis that the covariogram of x(t) follows a speciﬁc parametric model. The (tapered) periodogram may be used for a Whittle-type estimator leading to a corresponding test. Such a test-statistic, however, is not pivotal. To address this diﬀiculty, the authors introduce a transformation that leads to an asymptotic distribution, which is free of nuisance parameters. As a second result, the authors propose a bootstrap analogue of the transformation and conﬁrm its validity. Third, the authors analyze linear parametric regression models, where x(t) is the (unobserved) additive noise component. These contributions provide a foundation for further study of such multidimensional spatiotemporal processes and a mechanism for their use in econometric work in many diﬀerent applied ﬁelds.
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