The current Sars-Cov-2 epidemic has once more highlighted the need of accurate and timely food price information for decision makers, citizen and food supply chain stakeholders, especially in developing countries and in particular in Africa. The conventional methods adopted by official statistical offices do not allow the dissemination of such information in real time. Therefore, we used data collected by the JRC project Food Price Crowdsourcing Africa in Nigeria. In the first stage, we pre-processed data using the Spatial Post Sampling approach, and we then estimated a Reg-ARMA model to provide an accurate forecast of price trends.
Lucrezia Amerise, I., Solano Hermosilla, G., Nardelli, V., Arbia, G., Food prices forecast using post-sampled crowdsourced data with Reg-ARMA model: the case of Nigeria, Paper, in SIS 2022 - Book of Short Papers, (Caserta, 22-24 June 2022), Pearson, Roma 2022: 1172-1177 [https://hdl.handle.net/10807/325141]
Food prices forecast using post-sampled crowdsourced data with Reg-ARMA model: the case of Nigeria
Nardelli, Vincenzo;Arbia, Giuseppe
2022
Abstract
The current Sars-Cov-2 epidemic has once more highlighted the need of accurate and timely food price information for decision makers, citizen and food supply chain stakeholders, especially in developing countries and in particular in Africa. The conventional methods adopted by official statistical offices do not allow the dissemination of such information in real time. Therefore, we used data collected by the JRC project Food Price Crowdsourcing Africa in Nigeria. In the first stage, we pre-processed data using the Spatial Post Sampling approach, and we then estimated a Reg-ARMA model to provide an accurate forecast of price trends.
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