An MPEC Estimator for the Sequential Search Model

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.