This paper disaggregates energy consumption and GDP data according to end-use to analyze a broad number of developed and developing countries grouped in panels by similar characteristics. Panel long-run causality is assessed with a relatively under-utilized approach recommend by Canning and...

This paper provides estimation and inference methods for conditional average treatment effects (CATE) characterized by a high‐dimensional parameter in both homogeneous cross‐sectional and unit‐heterogeneous dynamic panel data settings. In our leading example, we model CATE by interacting...

Exploring the causal relationship between energy consumption and economic growth is important in energy economics. We performed an empirical re-examination of the causal interactions between these two factors in South Korea. As most previous studies in South Korea overlook the nonlinear behavior...

Despite a bourgeoning literature on the existence of a long-run relationship between energy consumption and economic growth, the findings have failed to establish clearly the direction of causation. A growing economy needs more energy, which is exacerbated by growing population. Evidence...

The aim of this article was to assess the empirical evidence of the nexus between GDP and energy consumption for Italy during the period 1970-2009, using a time series approach. After a brief introduction, a survey of the economic literature on this issue is shown, before discussing the data and...

Though there is a very large literature examining whether energy use Granger causes economic output or vice versa this literature is fairly inconclusive. Almost all existing studies use relatively short time series or panels with a relatively small time dimension. Additionally, many recent...

We consider a new method to estimate causal effects when a treated unit suffers a shock or an intervention, such as a policy change, but there is not a readily available control group or counterfactual. We propose a two-step approach where in the first stage an artificial counterfactual is...