Quantile partial adjustment model with application to predicting energy demand in China

Fenfen Cheng, Shanlin Yang and Kaile Zhou

Energy, 2020, vol. 191, issue C

Abstract: As the largest energy consumer, it is urgent for China to implement demand side management (DSM). This requires the accurate predictions of long-run energy demand and its short-run dynamic mechanism. To this end, we extend the conventional partial adjustment model into the framework of quantile regression and label it as quantile partial adjustment (QPA). The QPA model is able to investigate heterogeneous effects of drivers on energy demand, as well as to capture its whole conditional distribution. We conduct an empirical study on China’s energy demand using annual data from 1990 to 2017. The empirical results show that there exists obvious heterogeneous effects, for instance, the inverse-U shaped adjustment speed. Moreover, we design three different scenarios to produce conditional density forecasts of energy demand for the next 12 years. We notice that bimodal curves or even multimodal curves emerge under three different scenarios. These findings imply that there are several possible intervals for long-run energy demand, which leaves enough space to formulate rational and sustainable energy policies in China. The further discussion at the provincial level obtains similar results and shows the obvious heterogeneity across provinces, which highlights the importance to take into account regional differences in energy DSM.

Keywords: Energy demand; Partial adjustment model; Quantile regression; QPA; Conditional density forecasting (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544219322145
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:191:y:2020:i:c:s0360544219322145

DOI: 10.1016/j.energy.2019.116519

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().