摘要 : In this article, I describe thelclogit2command, an enhanced version oflclogit(Pacifico and Yoo, 2013,Stata Journal13: 625-639). Like its predecessor,lclogit2uses the expectation-maximization algorithm to fit latent class condition... 展开 In this article, I describe thelclogit2command, an enhanced version oflclogit(Pacifico and Yoo, 2013,Stata Journal13: 625-639). Like its predecessor,lclogit2uses the expectation-maximization algorithm to fit latent class conditional logit (LCL) models. But it executes the expectation-maximization algorithm's core algebraic operations in Mata, so it runs considerably faster as a result. It also allows linear constraints on parameters to be imposed more conveniently and flexibly. It comes with the parallel commandlclogitml2, a new stand-alone command that uses gradient-based algorithms to fit LCL models. Bothlclogit2andlclogitml2are supported by a new postestimation command,lclogitwtp2, that evaluates willingness-to-pay measures implied by fitted LCL models. 收起