MODIFIED RECURSIVE BAYESIAN ALGORITHM FOR ESTIMATING TIME-VARYING PARAMETERS IN DYNAMIC LINEAR MODELS

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Statistics in Transition New Series

Polish Statistical Association

Central Statistical Office of Poland

Subject: Economics , Statistics & Probability

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VOLUME 19 , ISSUE 2 (June 2018) > List of articles

MODIFIED RECURSIVE BAYESIAN ALGORITHM FOR ESTIMATING TIME-VARYING PARAMETERS IN DYNAMIC LINEAR MODELS

O. Olawale Awe / A. Adedayo Adepoju

Keywords : discounted variance, dynamic models, granularity range, estimation algorithm

Citation Information : Statistics in Transition New Series. Volume 19, Issue 2, Pages 239-258, DOI: https://doi.org/10.21307/stattrans-2018-014

License : (CC BY-NC-ND 4.0)

Published Online: 22-July-2018

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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.

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