Designing a Rice Price Forecast Model (Vector Autoregression Approach)

Document Type : Research Paper

Authors

1 Assistant Professor, Institute for Trade Studies and Research

2 Researcher at Institute for Trade Studies and Research

3 Associate Professor, Institute for Trade Studies and Research, Tehran, Iran

10.22034/iaes.2023.2007156.2006

Abstract

Considering the importance of rice and the significant share of its consumption in the household basket, the correct policy to control its price in the market will play a chief role in the well-being and food security of households. Therefore, checking the price trend and providing price forecasts for this product is significant. Hence, the present study aims to identify the influencing variables and investigate their impact on the price of rice and provide out-of-sample forecast (2023:05-2024:03) using vector autoregression method. Estimation of two models for Iranian Caspian rice and foreign Thai rice; using up-to-date and monthly data; Also, price forecasting of these two rice is one of the innovations of this study. Johanson-Jusilius's cointegration test confirmed the existence of a long-term relationship between variables. Then, the long-term and short-term (VECM) models were estimated and the error correction coefficients for the first and second models were -0.300 and -0.309, respectively, which are significant at the 1% level. Next, according to the results of impulse response functions, the shocks exchange rate and the price of the substitute product in model 1, and the shocks price of the substitute product and the global price index in model 2, have been more effective than other variables on the price fluctuations of Caspian and Thai rice. Finally, we estimated out-of-sample forecasts (with two currency scenarios). Based on the prediction evaluation criteria, the research models can accurately predict the price trend of Iranian and foreign rice.

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References

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