Journal Article10.1017/S026646660001152X
Proffessor T.W. Anderson
About: This article is published in Econometric Theory. The article was published on 01 Aug 1986.
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Citations
Trygve Haavelmo at the Cowles Commission
TL;DR: The early history of the Cowles Commission (CC), its close and intertwined relations with the Econometric Society (ES), and the influence and guidance of Ragnar Frisch are reviewed in this article.
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Trygve Haavelmo at the Cowles Commission
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Identification of a linear system from inexact data: A three-variable example☆
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References
Asymptotic Theory of Certain "Goodness of Fit" Criteria Based on Stochastic Processes
TL;DR: In this article, a general method for calculating the limiting distributions of these criteria is developed by reducing them to corresponding problems in stochastic processes, which in turn lead to more or less classical eigenvalue and boundary value problems for special classes of differential equations.
Formulation and estimation of dynamic models using panel data
TL;DR: In this article, the authors present a statistical analysis of time series regression models for longitudinal data with and without lagged dependent variables under a variety of assumptions about the initial conditions of the processes being analyzed.
3.2K
Estimation of Dynamic Models with Error Components
T. W. Anderson,Cheng Hsiao +1 more
TL;DR: In this paper, observations on N cross-section units at T time points are used to estimate a simple statistical model involving an autoregressive process with an additive term specific to the unit.
Estimation of the Parameters of a Single Equation in a Complete System of Stochastic Equations
T. W. Anderson,Herman Rubin +1 more
TL;DR: In this article, a method is given for estimating the coefficients of a single equation in a complete system of linear stochastic equations (see expression (2.1)), provided that a number of coefficients of the selected equation are known to be zero.
Asymptotic theory for principal component analysis
TL;DR: In this paper, the asymptotic distribution of the characteristic roots and vectors of a sample covariance matrix is given when the observations are from a multivariate normal distribution whose covariance matrices has characteristic roots of arbitrary multiplicity.