Computational methods for estimating multinomial, nested, and cross-nested logit models that account for semi-aggregate data
Published in Journal of Choice Modelling, 2018
We present a summary of important computational issues and opportunities that arise from the use of semi-aggregate data (where the explanatory data for choice scenarios are not necessarily unique for each decision-maker) in discrete choice models.
Recommended citation: Newman, J. P., Lurkin, V., & Garrow, L. A. (2018). "Computational methods for estimating multinomial, nested, and cross-nested logit models that account for semi-aggregate data." Journal of choice modelling, 26, 28-40. https://www.sciencedirect.com/science/article/abs/pii/S1755534517300179