Masking Identification of Discrete Choice Models Under Simulation Methods
Journal of Econometrics, Vol. 141, December 2007
36 Pages Posted: 3 Sep 2008
Date Written: December 2007
We present examples based on actual and synthetic datasets to illustrate how simulation methods can mask identification problems in the estimation of discrete choice models such as mixed logit. Simulation methods approximate an integral (without a closed form) by taking draws from the underlying distribution of the random variable of integration. Our examples reveal how a low number of draws can generate estimates that appear identified, but in fact, are either not theoretically identified by the model or not empirically identified by the data. For the particular case of maximum simulated likelihood estimation, we investigate the underlying source of the problem by focusing on the shape of the simulated log-likelihood function under different conditions.
Keywords: simulation methods, discrete choice, identification
JEL Classification: C15, C25
Suggested Citation: Suggested Citation
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By Amil Petrin