Recent advances in food insecurity classification have made analytical approaches to predict and inform response to food crises possible. This paper develops a predictive, statistical framework to identify drivers of food insecurity risk with simulation capabilities for scenario analyses, risk assessment and forecasting purposes. It utilizes a panel vector-autoregression to model food insecurity distributions of 15 Sub-Saharan African countries between October 2009 and February 2019. Statistical variable selection methods are employed to identify the most important agronomic, weather, conflict and economic variables. The paper finds that food insecurity dynamics are asymmetric and past-dependent, with low insecurity states more likely to transition to high insecurity states than vice versa. Conflict variables are more relevant for dynamics in highly critical stages, while agronomic and weather variables are more important for less critical states. Food prices are predictive for all cases. A Bayesian extension is introduced to incorporate expert opinions through the use of priors, which lead to significant improvements in model performance.
Detalhes
-
Autor
Wang,Dieter, Andree,Bo Pieter Johannes, Chamorro Elizondo,Andres Fernando, Spencer,Phoebe Girouard
-
Data do documento
2020/09/22
-
TIpo de documento
Documento de trabalho sobre pesquisa de políticas
-
No. do relatório
WPS9413
-
Nº do volume
1
-
Total Volume(s)
1
-
País
-
Região
-
Data de divulgação
2020/09/22
-
Disclosure Status
Disclosed
-
Nome do documento
Stochastic Modeling of Food Insecurity
-
Palavras-chave
food insecurity; conflict and violence; sustainable development goals; famine early warning system; expert opinion; Food Price Index; population share; standard normal distribution; european central bank; food security situation; variable rainfall; world food programme; million people; billion people; comments and feedback; crude death rate; lack of food; temporal aggregation; exogenous variable; staple food; standard deviation
- Exibir mais
Downloads
COMPLETAR RELATÓRIO
Versão oficial do documento (pode conter assinaturas, etc.)
- PDF oficial
- TXT*
- Total Downloads** :
- Download Stats
-
*A versão do texto é um OCR incorreto e está incluído unicamente em benefício de usuários com conectividade lenta.