73: Multivariate Modeling Techniques for Estimating Assistance Impacts from Daily Food Security Surveys

Music City Center 

Data collection for food security monitoring in both developed and developing countries is increasingly conducted via phone-based surveys. In particular, the World Food Programme partners with polling agencies to contact a daily random selection of households in countries of interest. Respondents are asked a variety of questions related to demographics, diet, and other indicators of food insecurity, poverty, and household stability. While the standard application of these surveys is to simply look for long-term trends in food security, the richness of the data also invites the development of multivariate models to assess the impacts of interventions and mitigation strategies. Conventional regression methods assume temporal independence in the residual noise given that the daily samples are essentially independent, but this ignores external factors that induce autocorrelation. Adding a latent time series to the model enables such temporal correlation to be estimated and extracted, thereby improving estimation of model parameters. We demonstrate the utility of this approach in the analysis of survey data collected between 2020 and 2022.

ARMA Process

Mixed Effect Model

Spectrum

Food Security

Conditional Expectation 

Statistics Without Borders