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IDENTIFYING RESTRICTIONS FOR FINITE PARAMETER CONTINUOUS TIME MODELS WITH DISCRETE TIME DATA

Published online by Cambridge University Press:  22 December 2015

Jason R. Blevins
Jason R. Blevins*
Affiliation:
Ohio State University
*
*Address correspondence to Jason R. Blevins Ohio State University, Department of Economics, 1945 N High St., 410 Arps Hall, Columbus, OH 43210, USA; e-mail: blevins.141@osu.edu
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Abstract

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This paper revisits the question of parameter identification when a linear continuous time model is sampled only at equispaced points in time. Following the framework and assumptions of Phillips (1973), we consider models characterized by first-order, linear systems of stochastic differential equations and use a priori restrictions on the model parameters as identifying restrictions. A practical rank condition is derived to test whether any particular collection of at least $\left\lfloor {n/2} \right\rfloor$ general linear restrictions on the parameter matrix is sufficient for identification. We then consider extensions to incorporate prior restrictions on the covariance matrix of the disturbances, to identify the covariance matrix itself, and to address identification in models with cointegration.

Type
MISCELLANEA
Information
Econometric Theory , Volume 33 , Issue 3 , June 2017 , pp. 739 - 754
Copyright
Copyright © Cambridge University Press 2015 

Footnotes

I am grateful to John Geweke, Robert de Jong, Matt Masten, Tucker McElroy, Peter Phillips, and three anonymous referees for useful comments and discussions.

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