Abstract
This work develops a model for minimum temperature in order to assess the weather related risk in agriculture industry. Non-linear autoregressive models with time-varying coefficients and volatility with various seasonal components and lags are compared in an appropriate model-selection algorithm using AIC. The optimal model is a time-varying autoregressive model which includes non-linear and seasonally-varying autoregressive terms as well as time-varying volatility. These models are then used to simulate future weather from which the probabilities of appropriate complex hazard events are estimated.
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Acknowledgments
We are indebted to Mr. Islami from Rafsanjan Weather Office for providing the data for this study. We are also thankful to Prof. Jim Zidek and Prof. Nhu Le for some fruitful discussions on the modeling and applications.
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Handling Editor: Pierre Dutilleul.
The first author was supported by research grants from the Japanese Society for Promotion of Science.
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Hosseini, R., Takemura, A. & Hosseini, A. Non-linear time-varying stochastic models for agroclimate risk assessment. Environ Ecol Stat 22, 227–246 (2015). https://doi.org/10.1007/s10651-014-0295-2
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DOI: https://doi.org/10.1007/s10651-014-0295-2