Impact of rainfall risk on rice production: realized volatility in mean model
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917985045708800 |
|---|---|
| author | Ghosh, Soham Mukhoti, Sujay Sharma, Pritee |
| author_facet | Ghosh, Soham Mukhoti, Sujay Sharma, Pritee |
| contents | Rural economies are largely dependent upon agriculture, which is greatly determined by climatic conditions such as rainfall. This study aims to forecast agricultural production in Maharashtra, India, which utilises annual data from the year 1962 to 2021. Since rainfall plays a major role with respect to the crop yield, we analyze the impact of rainfall on crop yield using four time series models that includes ARIMA, ARIMAX, GARCH-ARIMA and GARCH-ARIMAX. We take advantage of rainfall as an external regressor to examine if it contributes to the performance of the model. 1-step, 2-step, and 3-step ahead forecasts are obtained and the model performance is assessed using MAE and RMSE. The models are able to more accurately predict when using rainfall as a predictor compared to when solely dependant on historical production trends (more improved outcomes are seen in the ARIMAX and GARCH-ARIMAX models). As such, these findings underscore the need for climate-aware forecasting techniques that provide useful information to policymakers and farmers to aid in agricultural planning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_10121 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Impact of rainfall risk on rice production: realized volatility in mean model Ghosh, Soham Mukhoti, Sujay Sharma, Pritee Applications Primary 62M10, 62P12, Secondary 91B76, 62P20, 86A32 Primary 62M10, 62P12, Secondary 91B76, 62P20, 86A32 Primary 62M10, 62P12, Secondary 91B76, 62P20, 86A32 G.3; I.6.5; J.2 Rural economies are largely dependent upon agriculture, which is greatly determined by climatic conditions such as rainfall. This study aims to forecast agricultural production in Maharashtra, India, which utilises annual data from the year 1962 to 2021. Since rainfall plays a major role with respect to the crop yield, we analyze the impact of rainfall on crop yield using four time series models that includes ARIMA, ARIMAX, GARCH-ARIMA and GARCH-ARIMAX. We take advantage of rainfall as an external regressor to examine if it contributes to the performance of the model. 1-step, 2-step, and 3-step ahead forecasts are obtained and the model performance is assessed using MAE and RMSE. The models are able to more accurately predict when using rainfall as a predictor compared to when solely dependant on historical production trends (more improved outcomes are seen in the ARIMAX and GARCH-ARIMAX models). As such, these findings underscore the need for climate-aware forecasting techniques that provide useful information to policymakers and farmers to aid in agricultural planning. |
| title | Impact of rainfall risk on rice production: realized volatility in mean model |
| topic | Applications Primary 62M10, 62P12, Secondary 91B76, 62P20, 86A32 Primary 62M10, 62P12, Secondary 91B76, 62P20, 86A32 Primary 62M10, 62P12, Secondary 91B76, 62P20, 86A32 G.3; I.6.5; J.2 |
| url | https://arxiv.org/abs/2504.10121 |