Abstract
This paper reviews eight methods for estimating the form of non-stationary parameter variation from input-output data when the parameter variation model is unknown. The methods discussed are based upon error bounding and adaptive estimation, and assessment of the relative efficacy of each method is carried out by the use of simulation experiments with two models of parameter variation: deterministic parameter changes, and an autoregressive model of parameter changes. It is concluded from this analysis that estimation based upon exponential data weighting, the random walk model, and Sage-Husa filter will usually be best in a variety of situations.
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