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Identification and Normalization in Discrete Choice Models: Notes on ECON.721

This resource delves into the identification and normalization techniques in the Multinomial Probit (MNP) models, referencing works by Bunch, Albreit, Lerman, Manski, Hausman, and Wise. It covers the definition of identification, normalization approaches, and methods such as simulated method of moments, simulated scores, simulated maximum likelihood, and the limitations of simulation methods. The challenging estimation problem in MNP models due to high-dimensional integrals is also discussed along with solutions like the Crude frequency method. Illustrations and simulation algorithms provided. Limited to text language.

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Identification and Normalization in Discrete Choice Models: Notes on ECON.721

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Presentation Transcript

  1. Classical Discrete Choice Theory: Notes #2 ECON 721 Petra Todd

  2. Identification and Normalization in the MNP Model Reference: Bunch (1979)

  3. Digression on Identification

  4. Definition of Identification

  5. Illustration of how to impose normalization • Set 3 as the reference alternative

  6. Normalization approach of Albreit, Lerman and Manski (1978)

  7. How to solve the forecasting problem in MNP model

  8. Hausman and Wise (1978)

  9. Estimation methods for MNP models • Tend to be difficult to estimate because of high dimensional integrals • Methods • Simulated method of moments • Method of simulated scores • Simulated maximum likelihood

  10. Crude frequency method

  11. Simulated Method of Moments • McFadden (1989, Econometrica)

  12. Simulation algorithm

  13. Gauss-Newton (can be used to solve moment condition)

  14. Limitations of simulation methods

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