Counterfactual Estimation in Semiparametric Discrete‑Choice Models

Fri, 01 Jan 2021 00:00:00 +0000

In this book chapter, Chiong, Hsieh, and Shum examine counterfactual estimation techniques for semiparametric discrete‑choice models. They show how to obtain policy‑relevant counterfactuals when only partial information about preferences is available, illustrating the methods with examples from industrial organization.

Random Projection Estimation of Discrete-Choice Models with Large Choice Sets

Tue, 01 Jan 2019 00:00:00 +0000

Chiong and Shum tackle the computational challenges posed by discrete-choice models with large choice sets. Their random projection estimator dramatically reduces dimensionality, allowing researchers to analyze complex choice data efficiently. The approach is particularly useful for modern marketing and transportation applications involving many alternatives.

Discrete Choice |

Counterfactual Estimation in Semiparametric Discrete‑Choice Models

Random Projection Estimation of Discrete-Choice Models with Large Choice Sets