Modified recursive Bayesian algorithm for  estimating time-varying parameters in dynamic linear models

O.  Olawale Awe; A.  Adedayo Adepoju

doi:https://doi.org/10.21307/stattrans-2018-014

Modified recursive Bayesian algorithm for estimating time-varying parameters in dynamic linear models

O. Olawale Awe Department of Mathematical Sciences, Anchor University Lagos, Nigeria. , A. Adedayo Adepoju 2Department of Statistics, University of Ibadan, Nigeria. Statistics in Transition new series, vol. 19, 2018, 2, pages: 239-258 Published online: 1 June 2018 https://doi.org/10.21307/stattrans-2018-014

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ABSTRACT

Estimation in Dynamic Linear Models (DLMs) with Fixed Parameters (FPs) has been faced with considerable limitations due to its inability to capture the dynamics of most time-varying phenomena in econometric studies. An attempt to address this limitation resulted in the use of Recursive Bayesian Algorithms (RBAs) which is also affected by increased computational problems in estimating the Evolution Variance (EV) of the time-varying parameters. In this paper, we propose a modified RBA for estimating TVPs in DLMs with reduced computational challenges.

KEYWORDS

discounted variance, dynamic models, granularity range, estimation algorithm.

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