publications new

 

Abstract

The effectiveness of building energy codes is studied taking a long-run perspective. We focus on regulation´s impact on energy demand in high-end and low quality residences, i.e. the diffusion and the entry of "green" buildings in the housing market. We develop a measure for regulation intensity and apply this to a panel-error-correction regression model for energy requirements of a large sample of German apartment houses built between 1950-2005. We show that regulation is effective in saving energy. In particular, regulation pushes investors in the low quality housing market segment towards the technological frontier. Indirectly, it also affects the high-end housing market segment.

Abstract

In this paper, we demonstrate that using finite sample correction bootstrapping techniques is advisable in samples that cover less than two complete business cycles, even when high-frequency data seemingly provide a sufficient number of observations to overcome the small sample bias. This is particularly relevant in the current research environment. Because the recent financial crisis is considered as a structural break, research on current problems is often conducted using post-crisis data. That is, the available samples cover only a few years of data, often spanning only one business cycle or even less. We provide ample simulation-based evidence that samples of daily or monthly dynamic data covering periods of this magnitude are prone to a fairly substantial bias. Moreover, we are able to show that standard bootstrap-based bias correction techniques still work in those cases.