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The Estimation of Higher-Order Continuous Time Autoregressive Models

Published online by Cambridge University Press:  18 October 2010

A. C. Harvey  and
James H. Stock
A. C. Harvey
Affiliation:
London School of Economics and Harvard University
James H. Stock
Affiliation:
London School of Economics and Harvard University
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Abstract

A method is presented for computing maximum likelihood, or Gaussian, estimators of the structural parameters in a continuous time system of higherorder stochastic differential equations. It is argued that it is computationally efficient in the standard case of exact observations made at equally spaced intervals. Furthermore it can be applied in situations where the observations are at unequally spaced intervals, some observations are missing and/or the endogenous variables are subject to measurement error. The method is based on a state space representation and the use of the Kalman–Bucy filter. It is shown how the Kalman-Bucy filter can be modified to deal with flows as well as stocks.

Information

Type
Research Article
Information
Econometric Theory , Volume 1 , Issue 1 , April 1985 , pp. 97 - 117
Copyright
Copyright © Cambridge University Press 1985 

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References

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