Masking identification of discrete choice models under simulation methods

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 dis...

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Bibliographic Details
Published in:Journal of econometrics 2007-12, Vol.141 (2), p.683-703
Main Authors: Chiou, Lesley, Walker, Joan L.
Format: Article
Language:English
Subjects:
Applications
Discrete choice
Distribution
Distribution theory
Econometric models
Economic models
Estimates
Estimation
Exact sciences and technology
Identification
Insurance, economics, finance
Mathematical analysis
Mathematics
Maximum likelihood method
Measure and integration
Probability and statistics
Probability theory and stochastic processes
Random variables
Sciences and techniques of general use
Sequences, series, summability
Simulation
Simulation methods
Simulation techniques
Statistics
Studies
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Description
Summary: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.
ISSN:0304-4076
1872-6895
DOI:10.1016/j.jeconom.2006.10.012