List of Articles Bayesian Model Averaging (BMA)

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  1 - Determinants of Tourism Development in Developing Countries: A Bayesian Econometric Approach
  Hossein Panahi Sima Nasibparast
  Since tourism is increasingly developing in all over the world, it is focused more than before by most economists and policy makers. Accordingly, in order to make appropriate policies to improve tourism, investigating the determinants of tourism demand is very important More

  Since tourism is increasingly developing in all over the world, it is focused more than before by most economists and policy makers. Accordingly, in order to make appropriate policies to improve tourism, investigating the determinants of tourism demand is very important. According to the literature, there are a lot of possible variables which their effects on tourism demand in different economies have been emphasized by previous studies. Therefore, using the data obtained from Iran Provinces and applying Bayesian Model Averaging (BMA) method, this study tried to investigate the determinants of tourism demand in developing countries during 1995-2012. The results showed that the population of destination (as an index showing the size of market) and GDP (development index) are the most important variables which affect tourism demand. In addition, the variables related to infrastructures, communication facilities, international trade, quality of life and human capital (education and health) have positive effects on tourism development. On the contrary, some variables like high relative prices and poor quality of life such as pollution have reducing impact on tourism demand. According to the results, it is suggested to improve transport infrastructure, develop an efficient human capital by improving education and health status, and reduce pollution in cities. Manuscript profile
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  2 - Detecting the variables affecting on Bitcoin price: Bayesian Model Averaging and Weighted Averaging Least Square approach
  Mohammad kazem sadeghian kazem yavari abbas alavi rad
  The purpose of this paper detecting the variables affecting on Bitcoin price using daily Time series data from 2015 to 2019 invoking two method of Bayesian model Averaging and Weighted-Average Least Square. The results of this study show that the price variables of cryp More

  The purpose of this paper detecting the variables affecting on Bitcoin price using daily Time series data from 2015 to 2019 invoking two method of Bayesian model Averaging and Weighted-Average Least Square. The results of this study show that the price variables of cryptocurrencies with different creation mechanisms from Bitcoin and also the number of circulating cryptocurrencies with similar mechanism to Bitcoin and the volume of liquidity of US dollars affect the price of Bitcoin. On the other hand, the Forex market currency pairs, such as the dollar to Canadian dollar, the dollar to Australian dollar and the dollar to New Zealand dollar, which are less valuable than other major currency pairs in the Forex market, affect the price of Bitcoin. Also, the variables in the number of bitcoins, the number of cryptocurrencies in circulation with a different mechanism from bitcoin, the global price of gold and the number of searches for the word bitcoin in Google on its price have low coefficients. Overall, the results of the two methods of Bayesian averaging and Weighted Averaging Least Square are largely the same, and the use of the optimal pattern selection method confirms this. Manuscript profile