Preprint
SAUSS: Stochastic Approximation with Unbiased Simulated Scores for Limited Dependent Variable Models
arXiv
arXiv
08/26/2026
DOI: 10.48550/arxiv.2608.25304
Abstract
Multinomial choice models allow flexible substitution patterns but become computationally demanding with many alternatives or observations. With a fixed per-observation simulation budget, simulated maximum likelihood introduces simulation bias, while each optimization step requires a full-sample likelihood evaluation. We propose Stochastic Approximation with Unbiased Simulated Scores (SAUSS), an averaged stochastic approximation based on conditionally unbiased mini-batch score estimates. Each iteration uses a fixed mini-batch regardless of sample size. For multinomial probit, accept-reject sampling provides exact conditional draws and unbiased score estimates for any fixed number of accepted draws. Under local conditions, asymptotic theory for the averaged estimator and the partial-sum process of the SAUSS iterates incorporates mini-batch and simulation variability and supports random-scaling and plug-in inference. In simulations and an application, SAUSS gives comparable results in less than 1% of the computation time of simulated maximum likelihood. SAUSS extends to limited dependent variable models with conditional-expectation score representations and exact conditional sampling.
Details
- Title: Subtitle
- SAUSS: Stochastic Approximation with Unbiased Simulated Scores for Limited Dependent Variable Models
- Creators
- Sokbae LeeYuan LiaoMyung Hwan SeoYoungki Shin
- Resource Type
- Preprint
- Publication Details
- arXiv
- DOI
- 10.48550/arxiv.2608.25304
- ISSN
- 2331-8422
- Publisher
- arXiv
- Language
- English
- Date posted
- 08/26/2026
- Academic Unit
- Economics
- Record Identifier
- 9985220184702771
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