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‘Estimation of Discrete Choice Models Using DCM for Ox’

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Author Info
Eklöf, M.
Weeks, M.

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Abstract

DCM (Discrete Choice Models) is a package for estimating a class of discrete choice models. Written in Ox, DCM is a class that implements a wide range of discrete choice models including standard binary response models, with notable extensions including conditional mixed logit, mixed probit, multinomial probit, and random coefficient ordered choice models. The current version can handle both cross-section and static panel data. DCM represents an important development for the discrete choice computing environment in making available a broad range of models which are now widely used by academics and practitioners. Developed as a derived class of Modelbase, users may access the functions within DCM by either writing Ox programs which create and use an object of the DCM class, or use the program in an interactive fashion via OxPack in GiveWin. We demonstrate the capabilities of DCM by using a number of applications from the discrete choice literature.

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Publisher Info
Paper provided by Faculty of Economics, University of Cambridge in its series Cambridge Working Papers in Economics with number 0427.

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Length: 47
Date of creation: Apr 2004
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Handle: RePEc:cam:camdae:0427

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Related research
Keywords: discrete choice models; simulation methods; multinomial probit; mixed logit; ordinal response; revealed preference;

Find related papers by JEL classification:
C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software
C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models
C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Statistical Simulation Methods

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Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. Bhat, Chandra R., 2003. "Simulation estimation of mixed discrete choice models using randomized and scrambled Halton sequences," Transportation Research Part B: Methodological, Elsevier, vol. 37(9), pages 837-855, November. [Downloadable!] (restricted)
  2. Kenneth Train, 2000. "Halton Sequences for Mixed Logit," Department of Economics, Working Paper Series 1035, Department of Economics, Institute for Business and Economic Research, UC Berkeley. [Downloadable!]
    Other versions:
  3. McFadden, Daniel, 1989. "A Method of Simulated Moments for Estimation of Discrete Response Models without Numerical Integration," Econometrica, Econometric Society, vol. 57(5), pages 995-1026, September. [Downloadable!] (restricted)
    Other versions:
  4. repec:att:wimass:199217 is not listed on IDEAS
  5. repec:att:wimass:19895 is not listed on IDEAS
  6. Daniel McFadden & Kenneth Train, 2000. "Mixed MNL models for discrete response," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(5), pages 447-470. [Downloadable!]
  7. Alan Duncan & Melvyn Weeks, . "Non-Nested Models of Labour Supply with Discrete Choices," Discussion Papers 97/20, Department of Economics, University of York.
  8. Pakes, Ariel & Pollard, David, 1989. "Simulation and the Asymptotics of Optimization Estimators," Econometrica, Econometric Society, vol. 57(5), pages 1027-57, September. [Downloadable!] (restricted)
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