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

TL;DR

This paper introduces a novel sequential meta-posterior sampling method that significantly improves real-time Bayesian election forecasting by enabling efficient scenario and sensitivity analysis without repeated model refitting.

Contribution

The authors develop a meta-modeling and sequential sampling approach that allows real-time inference and sensitivity analysis in Bayesian election forecasting, overcoming computational bottlenecks of standard methods.

Findings

Substantial computational gains demonstrated in back-test.

Forecasts are sensitive to prior confidence but less to random walk scale.

Method uncovers non-trivial sensitivity patterns.

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 forecast shifts to model assumptions 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 such real-time analysis computationally expensive. To overcome the bottleneck, we introduce a meta-modeling strategy paired with a sequential sampling scheme; by traversing posterior meta-models, we enable real-time inference and structured scenario and sensitivity analysis without repeated refitting. In a back-test of the model, we show substantial computational gains and uncover non-trivial sensitivity patterns. For example, forecasts remain responsive to prior confidence in fundamentals-based forecasts, but less so to random walk scale; these help clarify the relative influence of polling data versus structural assumptions. Code is available at https://github.com/geonhee619/SMC-Sense.