Bayesian estimation of time-varying parameters in dynamic state space models in the presence of discounted evolution variance

O. O. Awe, A. A. Adepoju

Abstract


Considerable attention has been devoted in literature to the estimation of linear models with constant location parameters. However, many phenomena in real life situations exhibit a non-linear time-varying pattern, indicating a need to adopt Bayesian dynamic models and deal with the complexity involved in estimating the resulting time-varying parameters. In this paper, we present a novel application involving the estimation of time-varying parameters in dynamic state space regression models in the presence of discounted evolution variance. A conceptual review of the derivation of the posterior distribution of the time-varying parameters was done with the application of the proposed technique examined with simulated and real data. The results showed substantial time-variation in the slopeĀ  parameters associated with the studied location parameters, thereby highlighting the empirical relevance and advantage of the discounting method as well as its computationally less intensive nature.


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