Forecasting China's wastewater discharge using dynamic factors and mixed-frequency dataDing, L., Lv, Z., Han, M., Zhao, X. & Wang, W., Dec-2019, In : Environmental Pollution. 255, 1, 9 p., 113148.
Research output: Contribution to journal › Article › Academic › peer-review
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. (C) 2019 Elsevier Ltd. All rights reserved.
|Number of pages||9|
|Publication status||Published - Dec-2019|
- Wastewater discharge, MIDAS regression, Forecast combination, ECONOMIC-GROWTH, REGRESSION-MODELS, POLLUTION, PERFORMANCE, SUSTAINABILITY, VOLATILITY, EMISSIONS, NETWORKS, SCARCITY, IMPROVE