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.

FORECASTING REALIZED VOLATILITY DYNAMICALLY BASED ON ADJUSTED DYNAMIC MODEL AVERAGING (AMDA) APPROACH: EVIDENCE FROM CHINA’S STOCK MARKET

2019 ◽  
Vol 14 (04) ◽  
pp. 1950022
Author(s):  
PING YUAN
Keyword(s):  
Dynamic Model ◽  
Model Averaging ◽  
Realized Volatility ◽  
Time Varying ◽  
Alternative Strategies ◽  
Forecasting Accuracy ◽  
Time Varying Parameters ◽  
Constant Coefficients ◽  
The Individual ◽  
Dynamic Model Averaging

In this study, we forecast the realized volatility of the CSI 300 index using the heterogeneous autoregressive model for realized volatility (HAR-RV) and its various extensions. Our models take into account the time-varying property of the models’ parameters and the volatility of realized volatility. The adjusted dynamic model averaging (ADMA) approach, is used to combine the forecasts of the individual models. Our empirical results suggest that ADMA can generate more accurate forecasts than DMA method and alternative strategies. Models that use time-varying parameters have greater forecasting accuracy than models that use the constant coefficients.

scholarly journals Dynamic relationship between urban carbon dioxide emissions and economic growth

2020 ◽  
Keyword(s):  
Economic Growth ◽  
Carbon Dioxide ◽  
Energy Consumption ◽  
Granger Causality ◽  
Carbon Emissions ◽  
Carbon Dioxide Emissions ◽  
Emission Reduction ◽  
Carbon Emission ◽  
Carbon Dioxide Emission ◽  
Dynamic Relationship

<p>Urban economic development cannot be separated from energy consumption, and energy consumption directly leads to a large number of carbon emissions. It is of great significance to study the relationship between carbon dioxide emissions and economic growth for the implementation of energy conservation, emission reduction and the development of low-carbon economy in cities. A new method of dynamic relationship between urban carbon dioxide emission and economic growth is put forward. The carbon dioxide emission data in cities are calculated by using urban carbon dioxide emission measurement method. The data of economic attributes are obtained by using classification algorithm under uncertain data flow environment. Based on this data, a decoupling model of carbon emission and economic growth is constructed to measure economic growth elasticity of urban carbon emissions; Granger causality test model is established to analyze the Granger causality between urban carbon dioxide emissions and economic growth. The experimental results show that the growth rate of urban economy is obviously faster than that of carbon emissions. Economic growth is the Granger causality of carbon dioxide emissions. On the contrary, the implementation of carbon emission reduction measures will not hinder economic growth.</p>

scholarly journals Some Novel Bayesian Model Combination Schemes: An Application to Commodities Prices

2018 ◽  
Vol 10 (8) ◽  
pp. 2801 ◽  
Author(s):  
Krzysztof Drachal
Keyword(s):  
Model Selection ◽  
Dynamic Model ◽  
Bayesian Model ◽  
Model Averaging ◽  
Time Varying ◽  
Model Combination ◽  
Explanatory Variables ◽  
Time Varying Parameters ◽  
Dynamic Model Averaging ◽  
Commodities Prices

Forecasting commodities prices on vividly changing markets is a hard problem to tackle. However, being able to determine important price predictors in a time-varying setting is crucial for sustainability initiatives. For example, the 2000s commodities boom gave rise to questioning whether commodities markets become over-financialized. In case of agricultural commodities, it was questioned if the speculative pressures increase food prices. Recently, some newly proposed Bayesian model combination scheme has been proposed, i.e., Dynamic Model Averaging (DMA). This method has already been applied with success in certain markets. It joins together uncertainty about the model and explanatory variables and a time-varying parameters approach. It can also capture structural breaks and respond to market disturbances. Secondly, it can deal with numerous explanatory variables in a data-rich environment. Similarly, like Bayesian Model Averaging (BMA), Dynamic Model Averaging (DMA), Dynamic Model Selection (DMS) and Median Probability Model (MED) start from Time-Varying Parameters’ (TVP) regressions. All of these methods were applied to 69 spot commodities prices. The period between Dec 1983 and Oct 2017 was analysed. In approximately 80% of cases, according to the Diebold–Mariano test, DMA produced statistically significant more accurate forecast than benchmark forecasts (like the naive method or ARIMA). Moreover, amongst all the considered model types, DMA was in 22% of cases the most accurate one (significantly). MED was most often minimising the forecast errors (28%). However, in the text, it is clarified that this was due to some specific initial parameters setting. The second ”best” model type was MED, meaning that, in the case of model selection, relying on the highest posterior probability is not always preferable.

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.

Research on the Methods of Reducing Carbon Emissions in Airspace Optimization

2013 ◽  
Vol 718-720 ◽  
pp. 858-862
Author(s):  
Dai Wu Zhu ◽  
Zhi Heng Liu ◽  
Shu Yang ◽  
Jian Guo Xu
Keyword(s):  
Carbon Dioxide ◽  
Regression Analysis ◽  
Carbon Emissions ◽  
Carbon Dioxide Emissions ◽  
Carbon Emission ◽  
International Community ◽  
Saving Energy ◽  
Function Model ◽  
Specific Data

The international community is increasingly concerned about saving energy and less carbon dioxide emissions. But with growing air passenger and cargo traffic, the airspace tension highlights would inevitably lead to the increase in carbon emissions. However, there is little research on the methods of reducing carbon emission in airspace optimization. So this paper does some research in this field. Firstly this paper provides and exemplifies the method for decreasing the carbon emissions in airspace optimization. Secondly it puts forward the BPR function model to estimating the amount of carbon emissions of the method of increasing the number of air routes and uses the Regression analysis to confirm the parameters αβ. At last utilizing the specific data testifies the huge contribution of reducing the amount of carbon emissions from airspace optimization.

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