From frugal Jane to wasteful John: A quantile regression analysis of Swiss households’ electricity demand

Residential energy environmental Kuznets curve extended with non-linear temperature effects: a quantile regression for Andalusian (Spain) municipalities

“…Focusing on residential electricity consumption, this method has also been used when analyzing household electricity demand. Likewise, the study by Tilov et al (2020), also finds different temperature effects on electricity demand depending on the quantile level.…”

“…These studies have been improving the econometric estimates procedures, and recent residential Energy-EKC analyses use quantile regression or panel quantile regressions to take into account how heterogeneity affects the results, such as the studies by Borozan (2019) and Tilov et al (2020). These papers allow the relationships between energy consumption and income in the residential sector to be analyzed, differentiating by energy quantiles.…”

“…Secondly, temperature effects on residential energy consumption were also considered non-linear. Most previous studies show that temperature effects on residential energy consumption vary, according to the heating or cooling needs, and include two temperature variables in the equation model: the CDD and HDD variables (as in Tilov et al 2020). Beyond these considerations, other authors (such as ) also consider that each of these two variables may, in turn, have a non-linear effect on electricity consumption, and include CDD, HDD and their respective squared expressions in their studies.…”

Determinants of Household Electricity Demand in Rural India: A Case Study of the Impacts of Government Subsidies and Surcharges

“…So, we adopt the quantile regression developed by (Koenker nd Bassett, 1978). It produces more unbiased (Olsen et al, 2012) and robust estimates than the linear regression model when the data sets are large and it contains outliers (Tilov et al, 2020) and (Yeh et al, 2009). Quantile regression approach has been extensively used by (Tilov et al, 2020), (Romero et al, 2016), (Kostakis, 2020), (Athukorala et al, 2019), (Huebner et al, 2016) for detecting and quantification of the effects of determinants on selected quantile for the study concerned.…”

“…It produces more unbiased (Olsen et al, 2012) and robust estimates than the linear regression model when the data sets are large and it contains outliers (Tilov et al, 2020) and (Yeh et al, 2009). Quantile regression approach has been extensively used by (Tilov et al, 2020), (Romero et al, 2016), (Kostakis, 2020), (Athukorala et al, 2019), (Huebner et al, 2016) for detecting and quantification of the effects of determinants on selected quantile for the study concerned. Additionally, the box-plot of the units of electricity consumption of the households in Figure 1 illustrates that its distribution does not follow normal distribution.…”