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Modified Recursive Bayesian Algorithm For Estimating Time-Varying Parameters In Dynamic Linear Models

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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)

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.

Suggested Citation

  • Olawale Awe O. & Adedayo Adepoju A., 2018. "Modified Recursive Bayesian Algorithm For Estimating Time-Varying Parameters In Dynamic Linear Models," Statistics in Transition New Series, Statistics Poland, vol. 19(2), pages 258-293, June.
    • Handle: RePEc:vrs:stintr:v:19:y:2018:i:2:p:258-293:n:11
    DOI: 10.21307/stattrans-2018-014
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    References listed on IDEAS

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    Cited by:

    1. Awe Olushina Olawale & Adepoju Abosede Adedayo, 2020. "Change-point detection in CO2 emission-energy consumption nexus using a recursive Bayesian estimation approach," Statistics in Transition New Series, Statistics Poland, vol. 21(1), pages 123-136, March.

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