Several countries and regions have introduced mandatory minimum distances of wind turbines to nearby residential areas, in order to increase public acceptance of wind power. Germany's largest federal state Bavaria introduced such separation distances of ten times the height of new wind turbines...

Die verstärkte Nutzung der Windenergie an Land ist ein wesentlicher Baustein zur Erreichung der deutschen Klimaziele 2030. Neue Windparks hatten in letzter Zeit jedoch häufig mit Genehmigungsproblemen zu kämpfen, und der Zubau von Windenergieanlagen ist stark zurückgegangen. Das...

Decarbonizing the global energy matrix through investments in renewable energy (RE) is considered a pathway to mitigate the effects of global climate change. Auctions have become an increasingly popular policy instrument for this purpose. In the last few years, auctions have been rapidly adopted...

One of the perceived advantages of difference-in-differences (DiD) methods is that they do not explicitly restrict how units select into treatment. However, when justifying DiD, researchers often argue that the treatment is "quasi-randomly" assigned. We investigate what selection mechanisms are...

We propose a method of retrospective counterfactual imputation in panel data settings with later-treated and always-treated units, but no never-treated units. We use the observed outcomes to impute the counterfactual outcomes of the later-treated using a matrix completion estimator. We propose a...

Inference using difference-in-differences with clustered data requires care. Previous research has shown that t tests based on a cluster-robust variance estimator (CRVE) severely over-reject when there are few treated clusters, that different variants of the wild cluster bootstrap can...

We propose a generalization of the linear quantile regression model to accommodate possibilities afforded by panel data. Specifically, we extend the correlated random coefficients representation of linear quantile regression (e.g., Koenker, 2005; Section 2.6). We show that panel data allows the...

Inference for estimates of treatment effects with clustered data requires great care when treatment is assigned at the group level. This is true for both pure treatment models and difference-in-differences regressions. Even when the number of clusters is quite large, cluster-robust standard...

When there are few treated clusters in a pure treatment or difference-in-differences setting, t tests based on a cluster-robust variance estimator (CRVE) can severely over-reject. Although procedures based on the wild cluster bootstrap often work well when the number of treated clusters is not...

We propose a generalization of the linear quantile regression model to accommodate possibilities afforded by panel data. Specifically, we extend the correlated random coefficients representation of linear quantile regression (e.g., Koenker, 2005; Section 2.6). We show that panel data allows the...