Edited by Stephane Hess and Andrew Daly
Chapter 23: Numerical methods for optimization-based model estimation and inference
Researchers frequently use quantitative models to study and analyze choice behavior. They may obtain data on observed choice behavior through a variety of sources and methods (see Chapters 6 and 7) and then use the data to estimate or calibrate models that can be used for a variety of purposes, such as to develop and test theories, or to support the needs of decision makers. This chapter assumes a classical modeling framework where a dataset is viewed as a collection of observed outcomes from a data-generating process (DGP).
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