1. Forecasting China's wastewater discharge using dynamic factors and mixed-frequency data
- Author
-
Meng Han, Zhanlei Lv, Lili Ding, Wei Wang, and Xin Zhao
- Subjects
China ,Mixed frequency ,010504 meteorology & atmospheric sciences ,IMPROVE ,Health, Toxicology and Mutagenesis ,010501 environmental sciences ,Wastewater ,MIDAS regression ,Toxicology ,01 natural sciences ,Gross domestic product ,SUSTAINABILITY ,REGRESSION-MODELS ,POLLUTION ,Benchmark (surveying) ,Forecast combination ,Wastewater discharge ,EMISSIONS ,0105 earth and related environmental sciences ,ECONOMIC-GROWTH ,Environmental engineering ,Sampling (statistics) ,Regression analysis ,General Medicine ,Models, Theoretical ,PERFORMANCE ,NETWORKS ,SCARCITY ,Environmental science ,Sewage treatment ,VOLATILITY ,Forecasting - Abstract
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.
- Published
- 2019