Stochastic Modeling of Food Insecurity
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 simul...
| Main Authors: | , , , |
|---|---|
| Format: | Working Paper |
| Language: | English |
| Published: |
World Bank, Washington, DC
2020
|
| Subjects: | |
| Online Access: | http://documents.worldbank.org/curated/en/911801600788869914/Stochastic-Modeling-of-Food-Insecurity http://hdl.handle.net/10986/34511 |
| id |
okr-10986-34511 |
|---|---|
| recordtype |
oai_dc |
| spelling |
okr-10986-345112022-09-20T00:10:10Z Stochastic Modeling of Food Insecurity Wang, Dieter Andree, Bo Pieter Johannes Chamorro, Andres Fernando Girouard Spencer, Phoebe FOOD SECURITY FOOD INSECURITY FAMINE RISK VARIABLE SELECTION STOCHASTIC SIMULATION PANEL VECTOR AUTOREGRESSION EXPERT OPINION FOOD CRISIS WORLD FOOD PROGRAMME FORECASTING WEATHER FORECASTING BAYESIAN EXTENSION FOOD PRICES 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. 2020-09-24T21:10:19Z 2020-09-24T21:10:19Z 2020-09 Working Paper http://documents.worldbank.org/curated/en/911801600788869914/Stochastic-Modeling-of-Food-Insecurity http://hdl.handle.net/10986/34511 English Policy Research Working Paper;No. 9413 CC BY 3.0 IGO http://creativecommons.org/licenses/by/3.0/igo World Bank World Bank, Washington, DC Publications & Research Publications & Research :: Policy Research Working Paper Africa Sub-Saharan Africa |
| repository_type |
Digital Repository |
| institution_category |
Foreign Institution |
| institution |
Digital Repositories |
| building |
World Bank Open Knowledge Repository |
| collection |
World Bank |
| language |
English |
| topic |
FOOD SECURITY FOOD INSECURITY FAMINE RISK VARIABLE SELECTION STOCHASTIC SIMULATION PANEL VECTOR AUTOREGRESSION EXPERT OPINION FOOD CRISIS WORLD FOOD PROGRAMME FORECASTING WEATHER FORECASTING BAYESIAN EXTENSION FOOD PRICES |
| spellingShingle |
FOOD SECURITY FOOD INSECURITY FAMINE RISK VARIABLE SELECTION STOCHASTIC SIMULATION PANEL VECTOR AUTOREGRESSION EXPERT OPINION FOOD CRISIS WORLD FOOD PROGRAMME FORECASTING WEATHER FORECASTING BAYESIAN EXTENSION FOOD PRICES Wang, Dieter Andree, Bo Pieter Johannes Chamorro, Andres Fernando Girouard Spencer, Phoebe Stochastic Modeling of Food Insecurity |
| geographic_facet |
Africa Sub-Saharan Africa |
| relation |
Policy Research Working Paper;No. 9413 |
| description |
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. |
| format |
Working Paper |
| author |
Wang, Dieter Andree, Bo Pieter Johannes Chamorro, Andres Fernando Girouard Spencer, Phoebe |
| author_facet |
Wang, Dieter Andree, Bo Pieter Johannes Chamorro, Andres Fernando Girouard Spencer, Phoebe |
| author_sort |
Wang, Dieter |
| title |
Stochastic Modeling of Food Insecurity |
| title_short |
Stochastic Modeling of Food Insecurity |
| title_full |
Stochastic Modeling of Food Insecurity |
| title_fullStr |
Stochastic Modeling of Food Insecurity |
| title_full_unstemmed |
Stochastic Modeling of Food Insecurity |
| title_sort |
stochastic modeling of food insecurity |
| publisher |
World Bank, Washington, DC |
| publishDate |
2020 |
| url |
http://documents.worldbank.org/curated/en/911801600788869914/Stochastic-Modeling-of-Food-Insecurity http://hdl.handle.net/10986/34511 |
| _version_ |
1764481057830207488 |