An MPEC Estimator for the Sequential Search Model
Shinji Koiso, Suguru Otani

TL;DR
This paper introduces an MPEC-based estimator for sequential search models that improves numerical accuracy and avoids ad hoc assumptions, showing promising small-sample performance through Monte Carlo simulations.
Contribution
It develops a novel MPEC estimator for sequential search models that enhances accuracy and eliminates reliance on ad hoc components, with demonstrated advantages in small samples.
Findings
Performs better in small samples with lower bias and RMSE
Less effective in large samples compared to benchmarks
Generates equilibrium tables without ad hoc look-up tables
Abstract
This paper proposes a constrained maximum likelihood estimator for sequential search models, using the MPEC (Mathematical Programming with Equilibrium Constraints) approach. This method enhances numerical accuracy while avoiding ad hoc components and errors related to equilibrium conditions. Monte Carlo simulations show that the estimator performs better in small samples, with lower bias and root-mean-squared error, though less effectively in large samples. Despite these mixed results, the MPEC approach remains valuable for identifying candidate parameters comparable to the benchmark, without relying on ad hoc look-up tables, as it generates the table through solved equilibrium constraints.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsData Management and Algorithms · Web Data Mining and Analysis
MethodsHigh-Order Consensuses
