Masking Identification of Discrete Choice Models under Simulation Methods
AbstractWe 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.
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Bibliographic InfoPaper provided by Occidental College, Department of Economics in its series Occidental Economics Working Papers with number 5.
Length: 42 pages
Date of creation: Aug 2005
Date of revision: May 2006
simulation methods; discrete choice;
Find related papers by JEL classification:
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
- C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
This paper has been announced in the following NEP Reports:
- NEP-ALL-2006-04-29 (All new papers)
- NEP-DCM-2006-04-29 (Discrete Choice Models)
- NEP-ECM-2006-04-29 (Econometrics)
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