Directional Forecasting of Stock Market Premia Using Bayesian Dynamic Model Averaging and Selection

2015 ◽  
Author(s):  
Shamim Ahmed ◽  
Thanaset Chevapatrakul
Keyword(s):  
Stock Market ◽  
Dynamic Model ◽  
Model Averaging ◽  
Dynamic Model Averaging ◽  
Bayesian Dynamic Model

Forecasting Interconnections in International Housing Markets: Evidence from the Dynamic Model Averaging Approach

2020 ◽  
Vol 42 (1) ◽  
pp. 37-103
Author(s):  
Hardik A. Marfatia
Keyword(s):  
Dynamic Model ◽  
Model Averaging ◽  
Housing Markets ◽  
The United States ◽  
Forecasting Model ◽  
Change Over Time ◽  
The United Kingdom ◽  
Novel Approach ◽  
Dynamic Model Averaging ◽  
Over Time

In this paper, I undertake a novel approach to uncover the forecasting interconnections in the international housing markets. Using a dynamic model averaging framework that allows both the coefficients and the entire forecasting model to dynamically change over time, I uncover the intertwined forecasting relationships in 23 leading international housing markets. The evidence suggests significant forecasting interconnections in these markets. However, no country holds a constant forecasting advantage, including the United States and the United Kingdom, although the U.S. housing market's predictive power has increased over time. Evidence also suggests that allowing the forecasting model to change is more important than allowing the coefficients to change over time.

scholarly journals Forecasting Carbon Emissions with Dynamic Model Averaging Approach: Time-Varying Evidence from China

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Siqi Xu ◽  
Yifeng Zhang ◽  
Xiaodan Chen
Keyword(s):  
Carbon Dioxide ◽  
Dynamic Model ◽  
Carbon Emissions ◽  
Carbon Emission ◽  
Predictive Power ◽  
Carbon Dioxide Emission ◽  
Model Averaging ◽  
Predictive Ability ◽  
Time Varying ◽  
Dynamic Model Averaging

Although energy-related factors, such as energy intensity and energy consumption, are well recognized as major drivers of carbon dioxide emission in China, little is known about the time-varying impacts of other macrolevel nonenergy factors on carbon emission, especially those from macroeconomic, financial, household, and technology progress indicators in China. This paper contributes to the literature by investigating the time-varying predictive ability of 15 macrolevel indicators for China’s carbon dioxide emission from 1982 to 2017 with a dynamic model averaging (DMA) method. The empirical results show that, firstly, the explanatory power of each nonenergy predictor changes significantly with time and no predictor has a stable positive/negative impact on China’s carbon emissions throughout the whole sample period. Secondly, all these predictors present a distinct predictive ability for carbon emission in China. The proportion of industry production in GDP (IP) shows the greatest predictive power, while the proportion of FDI in GDP has the smallest forecasting ability. Interestingly, those Chinese household features, such as Engel’s coefficient and household savings rate, play very important roles in the prediction of China’s carbon emission. In addition, we find that IP are losing its predictive power in recent years, while the proportion of value-added of the service sector in GDP presents not only a leading forecasting weight, but a continuous increasing prediction power in recent years. Finally, the dynamic model averaging (DMA) method can produce the most accurate forecasts of carbon emission in China compared to other commonly used forecasting methods.

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