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Weather-induced Short-term Fluctuations of Economic Output

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  • Schreiber, Sven

Abstract

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.

Suggested Citation

  • Schreiber, Sven, 2018. "Weather-induced Short-term Fluctuations of Economic Output," VfS Annual Conference 2018 (Freiburg, Breisgau): Digital Economy 181622, Verein für Socialpolitik / German Economic Association.
    • Handle: RePEc:zbw:vfsc18:181622
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    References listed on IDEAS

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    1. Döhrn, Roland & an de Meulen, Philipp, 2015. "Weather, the Forgotten Factor in Business Cycle Analyses," Ruhr Economic Papers 539, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    2. Michael Boldin & Jonathan H. Wright, 2015. "Weather-Adjusting Economic Data," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 46(2 (Fall)), pages 227-278.
    3. Melissa Dell & Benjamin F. Jones & Benjamin A. Olken, 2014. "What Do We Learn from the Weather? The New Climate-Economy Literature," Journal of Economic Literature, American Economic Association, vol. 52(3), pages 740-798, September.
    4. Erik Haustein & Sven Schreiber, 2016. "Adjusting production indices for varying weather effects," IMK Working Paper 171-2016, IMK at the Hans Boeckler Foundation, Macroeconomic Policy Institute.
    5. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2004. "The MIDAS Touch: Mixed Data Sampling Regression Models," University of California at Los Angeles, Anderson Graduate School of Management qt9mf223rs, Anderson Graduate School of Management, UCLA.
    6. Claudia Foroni & Massimiliano Marcellino & Christian Schumacher, 2015. "Unrestricted mixed data sampling (MIDAS): MIDAS regressions with unrestricted lag polynomials," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(1), pages 57-82, January.
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    More about this item

    Keywords

    weather; business cycle; nowcasting; MIDAS;
    All these keywords.

    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications

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