Forecasting Survey Response Rates with Sequential Bayesian Modeling

Thomas M. Menino Convention & Exhibition Center 

Accurately projecting survey response rates is a critical part of planning and quality assurance for the Census Bureau's field operations. This paper presents a hierarchical Bayesian model that sequentially predicts the final response rate of ongoing surveys using daily cumulative response rate bounds. The model incorporates group-level effects for seasonal trends, yearly shifts, and survey-length variations (10- versus 11-day survey cycles), leveraging historical patterns and recent trends. Each day's posterior distribution of the final response rate serves as the prior for the subsequent day, allowing the model to dynamically update predictions as new data become available. The model is fitted on a ten-year dataset of monthly surveys, evaluating predictive performance using posterior predictive checks, backtesting, and leave-one-out cross-validation. This method offers a practical tool for real-time monitoring and management of survey operations and can be easily adapted to other applications requiring sequential forecasting in uncertain conditions.

Bayesian Modeling

Forecasting

Survey

Response Rates 

Government Statistics Section