Forecasting China's wastewater discharge using dynamic factors and mixed-frequency data

Ding, Lili; Lv, Zhanlei; Han, Meng; Zhao, Xin; Wang, Wei

Forecasting China's wastewater discharge using dynamic factors and mixed-frequency data

2019

Ding, Lili | Lv, Zhanlei | Han, Meng | Zhao, Xin | Wang, Wei

Forecasting wastewater discharge is the basis for wastewater treatment and policy formulation. This paper proposes a novel mixed-data sampling regression model, i.e., combination-MIDAS model to forecast quarterly wastewater emissions in China based on dynamic factors at different frequencies. The results show that a significant auto-correlation for wastewater emissions exists and that water consumption per ten thousand gross domestic product is the best predictor of wastewater emissions. The forecast performances of the combination-MIDAS models are robust and better than those of the benchmark models. Therefore, the combination-MIDAS models can better capture the characteristics of wastewater emissions, suggesting that the proposed method is a good method to deal with model misspecification and uncertainty for the control and management of wastewater discharge in China.

Показать больше [+]