Insurance plays a vital role of financial sector. Myanmar economy is dramatically developed under new nominally civilian government’s policies. This paper presents about how government policy changing in other factors effect on automobile insurance premium by using MCMC methods which is developed from Bayesian inference. The annual secondary data of registered vehicles, Gross Domestic Product (GDP), Population, premium of Third Party Liability Motor Insurance and Comprehensive Motor Insurance over the period of 1994 to 2014 were used in this study. The Metropolis-Hastings algorithm and Gibbs sampling are applied in this study. Raftery and Lewis convergence diagnostic are computed for MCMC sampler output to determine accuracy and probability of the parameter within a specified quantile to estimates the parameter are convergence or not. The result show that there have a positive relationship between automobile insurance and other factors such as GDP, registered vehicles and population. However, the number of registered is not mainly impact on automobile insurance although there has a mandatory motor insurance law in Myanmar.

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San, A. P. (2016). Factors affecting the number of registered automobile insurance in Myanmar based on Bayesian modeling using the MCMC procedure. International Journal of Humanities, Arts and Social Sciences, 2(2), 74-86.