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For nearly 25 years, advances in panel data and quantile regression were developed almost completely in parallel, with no intersection until the work by Koenker in the mid-2000s. The early theoretical work in statistics and economics raised more questions than answers, but it encouraged the development of several promising new approaches and research that offered a better understanding of the challenges and possibilities at the intersection of the literatures. Panel data quantile regression allows the estimation of effects that are heterogeneous throughout the conditional distribution of the response variable while controlling for individual and time-specific confounders. This type of heterogeneous effect is not well summarized by the average effect. For instance, the relationship between the number of students in a class and average educational achievement has been extensively investigated, but research also shows that class size affects low-achieving and high-achieving students differently. Advances in panel data include several methods and algorithms that have created opportunities for more informative and robust empirical analysis in models with subject heterogeneity and factor structure.

Forecasting electricity prices is a challenging task and an active area of research since the 1990s and the deregulation of the traditionally monopolistic and government-controlled power sectors. It is interdisciplinary by nature and requires expertise in econometrics, statistics or machine learning for developing well-performing predictive models, finance for understanding market mechanics, and electrical engineering for comprehension of the fundamentals driving electricity prices. Although electricity price forecasting aims at predicting both spot and forward prices, the vast majority of research is focused on short-term horizons which exhibit dynamics unlike in any other market. The reason is that power system stability calls for a constant balance between production and consumption, while being dependent on weather (in terms of demand and supply) and business activity (in terms of demand only). The recent market innovations do not help in this respect. The rapid expansion of intermittent renewable energy sources is not offset by the costly increase of electricity storage capacities and modernization of the grid infrastructure. On the methodological side, this leads to three visible trends in electricity price forecasting research. First, there is a slow but more noticeable tendency to consider not only point but also probabilistic (interval, density) or even path (also called ensemble) forecasts. Second, there is a clear shift from the relatively parsimonious econometric (or statistical) models toward more complex and harder to comprehend but more versatile and eventually more accurate statistical and machine learning approaches. Third, statistical error measures are regarded as only the first evaluation step. Since they may not necessarily reflect the economic value of reducing prediction errors, in recent publications they tend to be complemented by case studies comparing profits from scheduling or trading strategies based on price forecasts obtained from different models.