Efficient scenario analysis in real-time Bayesian election forecasting via sequential meta-posterior sampling

Abstract

Bayesian aggregation lets election forecasters combine diverse sources of information, such as state polls and economic and political indicators: as in our collaboration with The Economist magazine. However, the demands of real-time posterior updating, model checking, and communication introduce practical methodological challenges. In particular, sensitivity and scenario analysis help trace intricate dependencies and understand model behavior. Yet, under standard Markov chain Monte Carlo, even small tweaks to the model (e.g., in priors, data, hyperparameters) require full refitting, making it computationally expensive in real time. We introduce a meta-modeling strategy paired with a sequential sampling scheme; by traversing posterior meta-models, we enable real-time structured analyses without repeated refitting. In a back-test of the model, we demonstrate substantial computational gains, and show how seemingly innocuous data-wrangling choices can unintentionally introduce partisan asymmetries into the model's mechanisms for handling systematic polling error. Code is available at https://github.com/geonhee619/SMC-Sense.