Beiträge zur Jahrestagung des Vereins für Socialpolitik 2018: Digitale Wirtschaft - Session: Environmental Economics IV D06-V3
We contribute to the recent literature on the economic effects of those weather conditions that deviate from their regular seasonal pattern. To this end we use local temperature and snow measurements across Germany to analyze their impact on German monthly total industrial and construction-sector production. We find noticeable effects of the various (linear and nonlinear, contemporaneous and dynamic) weather regressors, which in the –seasonally adjusted– construction sector growth data imply an extra explanatory power of more than 50% of the variation, compared to benchmark predictive regressions. As expected, the impact is quite a bit less in total industrial production. From our estimates we obtain (seasonally as well as) weather adjusted production series, and our regression-based approach also yields confidence intervals for these adjustments. The estimated adjustments are quantitatively relevant also for broad output (quarterly GDP). In a mixed-frequency framework we find some value of the estimated monthly weather impact for quarterly GDP nowcasts in (quasi) real time.