Welcome to the new version of CaltechAUTHORS. Login is currently restricted to library staff. If you notice any issues, please email coda@library.caltech.edu
Published October 10, 2017 | Submitted
Report Open

Estimation of Dynamic Models with Error Components

Abstract

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. Different assumptions about the initial conditions are (a) initial state fixed, (b) initial state random, (c) the unobserved individual effect independent of the unobserved dynamic process with the initial value fixed, and (d) the unobserved individual effect independent of the unobserved dynamic process with initial value random. Asymptotic properties of the maximum likelihood and "covariance" estimators are obtained when T → ∞ and when N → ∞. The relationship between the pseudo and conditional maximum likelihood estimators is clarified. A simple consistent estimator that is independent of the initial conditions and the way in which T or N → ∞ is also suggested.

Additional Information

This work was supported by National Science Foundation Grants SES79-13976 and SES80-07576 at the Institute for Mathematical Studies in the Social Sciences, Stanford University and Social Sciences and Humanities Research Council of Canada Grant 410-80-0080 at the Institute for Policy Analysis, University of Toronto. This technical report was completed while the first author was a Sherman Fairchild Distinguished Scholar at the California Institute of Technology. The authors are indebted to James Powell for assistance in preparing this paper. Published as Anderson, Theodore Wilbur, and Cheng Hsiao. "Estimation of dynamic models with error components." Journal of the American statistical Association 76.375 (1981): 598-606.

Attached Files

Submitted - sswp336.pdf

Files

sswp336.pdf
Files (1.1 MB)
Name Size Download all
md5:76120d2d918289d4486c22a8d4d0252b
1.1 MB Preview Download

Additional details

Created:
August 19, 2023
Modified:
January 14, 2024