Abstract
In this paper we develop two sequential or "on-line" estimation schemes in the time domain for dynamic shock-error models which are special cases of errors-in-variables models. Our approach utilizes a state space representation of the model, Kalman filtering techniques and on-line algorithms. The first on-line algorithm is based on the Expectation Maximization (EM) algorithm and uses stochastic approximations to maximize the Kullback Leibler (KL) information measure. The second on-line algorithm we propose is a gradient based scheme and uses stochastic approximations to maximize the log likelihood. In comparison to the off-line Maximum Likelihood estimation scheme used in [1], our on-line algorithms have significantly reduced computational costs and negligible memory requirements.
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