lclogit2: An enhanced module to estimate latent class conditional logit models

Yoo, Hong Il (2019): lclogit2: An enhanced module to estimate latent class conditional logit models.

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.

• Item Type: MPRA Paper
• Language: English
• Keywords: lclogit2, lclogitml2, lclogitwtp2, lclogit, mixlogit, fmm, finite mixture, mixed logit
• Subjects: C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C35 - Discrete Regression and Qualitative Choice Models ; Discrete Regressors ; Proportions; C - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C61 - Optimization Techniques ; Programming Models ; Dynamic Analysis; C - Mathematical and Quantitative Methods > C8 - Data Collection and Data Estimation Methodology ; Computer Programs > C87 - Econometric Software

References

See the paper.