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

Ding, L., Lv, Z., Han, M., Zhao, X. & Wang, W., Dec-2019, In : Environmental Pollution. 255, 1, 9 p., 113148.

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  • Forecasting China's wastewater discharge

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DOI

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.
Original languageEnglish
Article number113148
Number of pages9
JournalEnvironmental Pollution
Volume255
Issue number1
Publication statusPublished - Dec-2019

    Keywords

  • Wastewater discharge, MIDAS regression, Forecast combination, ECONOMIC-GROWTH, REGRESSION-MODELS, POLLUTION, PERFORMANCE, SUSTAINABILITY, VOLATILITY, EMISSIONS, NETWORKS, SCARCITY, IMPROVE

ID: 108097083