In this article, I describe the lclogit2 command, an enhanced version of lclogit (Pacifico and Yoo, 2013, Stata Journal 13: 625–639). Like its predecessor, lclogit2 uses 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 command lclogitml2, a new stand-alone command that uses gradient-based algorithms to fit LCL models. Both lclogit2 and lclogitml2 are supported by a new postestimation command, lclogitwtp2, that evaluates willingness-to-pay measures implied by fitted LCL models.

在本文中，我介绍了lclogit2命令，它是lclogit的增强版本（Pacifico和Yoo，2013年，Stata Journal 13：625–639）。与其前身一样，lclogit2使用期望最大化算法来拟合潜在类条件对数（LCL）模型。但是它在Mata中执行了期望最大化算法的核心代数运算，因此运行速度大大提高。它还允许更方便，更灵活地对参数进行线性约束。它带有并行命令lclogitml2，这是一个新的独立命令，该命令使用基于梯度的算法来拟合LCL模型。无论lclogit2和lclogitml2受新的后估算命令lclogitwtp2支持，该命令可评估拟合的LCL模型所隐含的支付意愿度量。