In recent years, revenue management research developed increasingly complex demand forecasts to model customer choice. While the resulting systems should easily outperform their predecessors, it appears difficult to achieve substantial improvement in practice. At the same time, interest in robust revenue maximization is growing. From this arises the challenge of creating versatile and computationally efficient approaches to estimate demand and quantify demand
uncertainty. Motivated by this challenge, this paper introduces and benchmarks two filter-based demand estimators: the unscented Kalman filter and the particle filter. It documents a computational study, which is set in the airline industry and compares the estimators’ efficiency to that of sequential estimation and maximum-likelihood estimation. We quantify estimator efficiency through the posterior Cramer–Rao bound and compare revenue performance to the revenue opportunity. Both indicate that unscented Kalman filter and maximum-likelihood estimation outperform the alternatives. In addition, the Kalman filter requires comparatively little computational effort to update and quantifies demand uncertainty.