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

Olawale Awe O. () and Adedayo Adepoju A. ()
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Olawale Awe O.: Department of Mathematical Sciences, Anchor University Lagos, Lagos, ; Nigeria
Adedayo Adepoju A.: Department of Statistics, University of Ibadan, Ibadan, ; Nigeria

Statistics in Transition New Series, 2018, vol. 19, issue 2, 258-293

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. (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:stintr:v:19:y:2018:i:2:p:258-293:n:11

DOI: 10.21307/stattrans-2018-014

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