Estimating Two-Sided Logit Models

Logan (1996a) introduced a new micro-behavioral model of employment opportunity and choice, and a multivariate statistical method based on the micro-behavioral model. This article considers the connection between the behavioral model and the two-sided logit (TSL) statistical method in more detail than the original paper, discussing issues of parameter identification, model constraints, data reduction, and practical estimation. The article compares the EM gradient algorithm used in Logan (1996a) with an accelerated EM gradient algorithm and with a public-domain quasi-Newton algorithm. The latter two algorithms, now incorporated in a single program, greatly enhance the practicality of TSL modeling. Strategies for further development of TSL methods are also considered.