Abstract
In the present article, a linear approach of signal smoothing for nonlinear score-driven models is suggested, by using results from the literature on minimum mean squared error signals. Score-driven location, trend, and seasonality models with constant and score-driven scale parameters are used, for which the parameters are estimated by using the maximum likelihood method. The smoothing procedure is computationally fast, and it uses closed-form formulas for smoothed signals. Applications for monthly data of the seasonally adjusted and the not seasonally adjusted the United States inflation rate variables for the period of 1948–2020 are presented.
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