Predicting Food Crises
| Autor: | Bo Andree, Andres Chamorro, Aart Kraay, Phoebe Spencer, Wang, D. |
|---|---|
| Přispěvatelé: | Spatial Economics, Tinbergen Institute, Finance |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Food insecurity
Humanitarian intervention STATISTICAL MODEL FOOD CRISIS FOOD SECURITY TARGETING FOOD INSECURITY EXTREME EVENT Humanitarian finance Famine risk Prediction models UNBALANCED DATA Machine Learning HUMANITARIAN CRISIS FAMINE FORECASTING SDG 13 - Climate Action Poverty COST-SENSITIVE LEARNING |
| Zdroj: | None Andree, B P J, Chamorro, A, Kraay, A, Spencer, P & Wang, D 2020 ' Predicting Food Crises ' Policy Research Working Papers, vol. 9413, World Bank, pp. 1 . < http://documents.worldbank.org/curated/en/304451600783424495/Predicting-Food-Crises > Vrije Universiteit Amsterdam |
| Popis: | Globally, more than 130 million people are estimated to be in food crisis. These humanitarian disasters are associated with severe impacts on livelihoods that can reverse years of development gains. The existing outlooks of crisis-affected populations rely on expert assessment of evidence and are limited in their temporal frequency and ability to look beyond several months. This paper presents a statistical forecasting approach to predict the outbreak of food crises with sufficient lead time for preventive action. Different use cases are explored related to possible alternative targeting policies and the levels at which finance is typically unlocked. The results indicate that, particularly at longer forecasting horizons, the statistical predictions compare favorably to expert-based outlooks. The paper concludes that statistical models demonstrate good ability to detect future outbreaks of food crises and that using statistical forecasting approaches may help increase lead time for action. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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