Can Paddy Growing Phase Produce an Accurate Forecast of Paddy Harvested Area in Indonesia? Analysis of the Area Sampling Frame Results

Kadir Kadir and Octavia Rizky Prasetyo
Authors registered in the RePEc Author Service: Kadir Ruslan ()

MPRA Paper from University Library of Munich, Germany

Abstract: Our study aims to evaluate the accuracy of the forecasts produced based on the paddy growing phase obtained from the results of the Area Sampling Frame (ASF) Survey and, as a comparison, proposes an alternative forecast method taking into account the seasonal pattern and hierarchical structure of the national paddy harvested area estimation obtained from the ASF to improve the accuracy. In doing so, we calculated the MAPE by comparing the realization of paddy harvested area during the period January to September 2022 with their forecasts produced from the area of generative, late vegetative, and early vegetative phases. We also implemented a Hierarchical forecasting method on monthly data of the harvested area from January 2018 to August 2022 for all provinces. Specifically, we applied the bottom-up method for the reconciliation and the rolling window method to produce a three-consecutive month forecast for the period January to September 2022. We found that the accuracy prediction based on the paddy growing phase is moderately accurate. The combination of the bottom-up reconciliation method and the SARIMA model produces a much better accuracy for the national figure of paddy harvested area as shown by a lower MAPE. Our findings suggest that the Hierarchical forecasting method could be an alternative for the prediction of harvested area based on the ASF results other than the prediction obtained from the standing crops.

Keywords: ASF; Hierarchical; forecasting; paddy; SARIMA (search for similar items in EconPapers)
JEL-codes: C1 C18 C40 Q1 Q10 (search for similar items in EconPapers)
Date: 2023-08-12, Revised 2023-09-15
New Economics Papers: this item is included in nep-for, nep-inv and nep-sea
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Published in Proceeding of International Conference on Data Science and Official Statistics 1.2023(2023): pp. 746-755

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