{"title":"An MPEC Estimator for the Sequential Search Model","authors":"Shinji Koiso, Suguru Otani","doi":"arxiv-2409.04378","DOIUrl":null,"url":null,"abstract":"This paper proposes a constrained maximum likelihood estimator for sequential\nsearch models, using the MPEC (Mathematical Programming with Equilibrium\nConstraints) approach. This method enhances numerical accuracy while avoiding\nad hoc components and errors related to equilibrium conditions. Monte Carlo\nsimulations show that the estimator performs better in small samples, with\nlower bias and root-mean-squared error, though less effectively in large\nsamples. Despite these mixed results, the MPEC approach remains valuable for\nidentifying candidate parameters comparable to the benchmark, without relying\non ad hoc look-up tables, as it generates the table through solved equilibrium\nconstraints.","PeriodicalId":501293,"journal":{"name":"arXiv - ECON - Econometrics","volume":"29 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - ECON - Econometrics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.04378","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
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.