Optimal Estimation of Discrete Choice Demand Models with Consumer and Product Data

Paul L. E. Grieco, Charles Murry, Joris Pinkse and Stephan Sagl

No 33397, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: We propose a conformant likelihood estimator with exogeneity restrictions (CLEER) for random coefficients discrete choice demand models that is applicable in a broad range of data settings. It combines the likelihoods of two mixed logit estimators—one for consumer level data, and one for product level data—with product level exogeneity restrictions. Our estimator is both efficient and conformant: its rates of convergence will be the fastest possible given the variation available in the data. The researcher does not need to pre-test or adjust the estimator and the inference procedure is valid across a wide variety of scenarios. Moreover, it can be tractably applied to large datasets. We illustrate the features of our estimator by comparing it to alternatives in the literature.

JEL-codes: C13 C18 L0 (search for similar items in EconPapers)
Date: 2025-01
New Economics Papers: this item is included in nep-com, nep-dcm and nep-ecm
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