Seasonal Autoregressive Models for Estimating the Probability of Frost in Rafsanjan

Authors

1 Department of industrial engineering, Yazd University, Yazd, Iran

2 Division of Biostatistics, University of Southern California, USA

Abstract

This work develops a statistical model to assess the frost risk in Rafsanjan, one of the largest pistachio production regions in the world. These models can be used to estimate the probability that a frost happens in a given time-period during the year; a frost happens after 10 warm days in the growing season. These probability estimates then can be used for: (1) assessing the agroclimate risk of investing in this industry; (2) pricing of weather derivatives. Autoregressive models with time-varying coefficients and different lags are compared using AIC/BIC/AICc and cross validation criterions. The optimal model is an AR (1) with both intercept and the “autoregressive coefficients” vary with time. The long-term trends are also accounted for and estimated from data. The optimal models are then used to simulate future weather from which the probabilities of appropriate hazard events are estimated.

Keywords