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Testing for increasing weather risk

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Abstract

It is an undisputed fact that weather risk increases over time due to climate change. However, qualification of this statement with regard to the type of weather risk and geographical location is needed. In this paper we compare alternative tests for trend detection and discuss their sensitivity. We use local t tests, change point tests and Mann–Kendall tests to analyze the trends of weather risk indices that are relevant from an agricultural viewpoint. Local test procedures offer more information about the timing and the kind of change in weather risk than global tests do. We also use quantile regression to analyze changes in the tails of weather index distributions. These methods are applied to temperature and rainfall based weather indices in three different climatic zones. Our results show that weather risk follows different patterns depending on the type of risk and the location. We also find differences in the sensitivity of the statistical test procedures.

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Notes

  1. Moreover, it is possible to conduct a real time analysis with the suggested local tests, i.e., whenever a new observation is available, one can update the trend prediction for the next year.

  2. A sensitivity analysis for the hyper parameters γ, κ, L was conducted using simulated data with parameters calibrated to real data of the GDD in Taipei, Taiwan. We found that larger values of γ lead to a larger delay in the detection of trend and less false alarms for all three local tests. The higher value of κ is the later the change alarm will terminate. Finally, a smaller window size makes the results more sensitive, i.e. the number of reported changes as well as the number of false alarms increases. Analogous observations have been made in studies on adaptive estimation by. Our conclusion is that within a given range of the hyper parameters, type I errors can be controlled and are quite stable, while the type II errors are also moderately small. See also (Härdle et al. 2011b) for a discussion of the choice of hyper parameters.

  3. Taipei weather data was obtained from Central Weather Bureau of Taiwan (http://www.cwb.gov.tw), Berlin data from German Weather Service (http://www.wetterdienst.de) and Mason data from Wilson et al. (2007). The coordinates of the three weather stations are Lat. 52.466, Long. 13.4 in Berlin, Lat. 25.033, Long. 121.517 in Taipei and Lat. 43.15, Long. −93.199 in Mason.

  4. The longest consecutive period of missing rainfall data amounts to 3 days.

  5. Note that we want to quantify the yield risk of a diversified crop production in general. The analysis of the weather risk exposure of a particular crop would require a more narrow definition of the weather indices.

  6. Taipei has two vegetation periods in contrast to Germany and the US. However, the period March-October covers also the first vegetation period in that region. That means, the weather risk exposure of agricultural producers in the second vegetation period is not captured with our specific definition of the weather risk indicators.

  7. Autocorrelation test results are available from the authors upon request. As a reviewer points out inferences based on the correlogram or autocorrelation function (ACF) may not have a good finite sample performance. Nevertheless, the local randomness for a sample is an accepted fact for time series analysis (e.g. Dawdy and Matalas 1964; Anderson 1941).

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Acknowledgments

We like to thank to two anonymous reviewers for their valuable comments. This research was supported by the Deutsche Forschungsgemeinschaft through the SFB 649 “Economic Risk”.

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Authors and Affiliations

  1. Ladislaus von Bortkiewicz Chair of Statistics, School of Business and Economics, Humboldt-Universität zu Berlin, Berlin, Germany

    W. Wang & W. K. Härdle

  2. Department of Agricultural Markets, Marketing and World Agricultural Trade, Leibniz Institute of Agricultural Development in Central and Eastern Europe (IAMO), Halle (Saale), Germany

    I. Bobojonov

  3. Department of Agricultural Economics, Farm Management Group, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099, Berlin, Germany

    M. Odening

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  1. W. Wang
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Correspondence to M. Odening.

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Wang, W., Bobojonov, I., Härdle, W.K. et al. Testing for increasing weather risk. Stoch Environ Res Risk Assess 27, 1565–1574 (2013). https://doi.org/10.1007/s00477-013-0692-3

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