1. lclogit2: An Enhanced Module to Estimate Latent Class Conditional Logit Models
- Author
-
Hong Il Yoo
- Subjects
Core (game theory) ,Finite mixture ,Computer science ,Mixed logit ,Algebraic operation ,Logit ,Expectation–maximization algorithm ,Algorithm ,Class (biology) ,Standalone program - Abstract
This paper describes Stata command lclogit2, an enhanced version of lclogit (Pacifico and Yoo, 2013). Like its predecessor, lclogit2 uses the Expectation-Maximization (EM) algorithm to estimate latent class conditional logit (LCL) models. But it executes the EM algorithm's core algebraic operations in Mata, and runs considerably faster as a result. It also allows linear constraints on parameters to be imposed in a more convenient and flexible manner. It comes with parallel command lclogitml2, a new standalone program that uses gradient-based algorithms to estimate LCL models. Both lclogit2 and lclogitml2 are supported by a new postestimation tool, lclogitwtp2, that evaluates willingness-to-pay measures implied by estimated LCL models.
- Published
- 2019