Discrete Choice Models

In discrete choice models, a typical individual chooses an alternative out of a set with a finite number of alternatives.  Each alternative is characterized by a vector of attributes.  The individual is assumed to choose the one that maximizes his utility over the set of alternatives.  The utility that the individual derives from each alternative is assumed to possess observable and unobservable components.  Estimation of discrete choice models allow one to predict the demand for new products, when these are characterized by a vector of attributes that existent products possess.  They also allow one to make welfare calculations, once the utilities of the consumers are estimated.


Matzkin, R.L. (2019) "Constructive Identification in Some Nonseparable Discrete Choice Models,” Journal of Econometrics, 2019, Vol. 211 (1), p. 83-103.

Matzkin, R.L. (2016) "On Independence Conditions in Nonseparable Models: Observable and Unobservable Instruments,” Journal of Econometrics, 2016, Vol. 191(2), p. 302-311.

Blundell, R. and R.L. Matzkin (2014) "Control Functions in Nonseparable Simultaneous Equations Models," Quantitative Economics, Vol. 5, No. 2, 271-295.

Matzkin, R.L. (2012) “Identification of Limited Dependent Variable Models with Simultaneity and Unobserved Heterogeneity,” Journal of Econometrics, Vol. 166, No. 1, 106-115.

Matzkin, R.L. (2008) “Non-parametric Structural Models” in The New Palgrave Dictionary in Economics, edited by S. Durlauf and L. Blume, Macmillan.  Reprinted in Microeconometrics (2010), edited by S. Durlauf and L. Blume, Macmillan.

Matzkin, R.L. (2007) “Nonparametric Identification,” Chapter 73 in Handbook of Econometrics, Vol. 6b, edited by J.J. Heckman and E.E.  Leamer, Elsevier B.V., 5307-5368. 

Matzkin, R.L. (2007)Nonparametric Survey Response Errors,” International Economic Review, No. 48, No. 4, 1411-1427.

Matzkin, R.L. (2007) “Heterogeneous Choice,” in Advances in Economics and Econometrics, Theory and Applications, Ninth World Congress of the Econometric Society, edited by R. Blundell, W. Newey, and T. Persson, Cambridge University Press, 75-110.

Altonji, J. and R.L. Matzkin (2005), “Cross Section and Panel Data Estimators for Nonseparable Models with Endogenous Regressors,” Econometrica, Vol. 73, No. 3, leading article, p. 1053-1102.

Briesch, R., P. Chintagunta, and R.L. Matzkin (2002), Semiparametric Estimation of Choice Brand Behavior,” Journal of the American Statistical Association, Vol. 97, No. 460, Applications and Case Studies, p. 973-982.

Matzkin, R.L. (1994),  “Restrictions of Economic Theory in Nonparametric Methods,”  Handbook of Econometrics, Vol. 4, edited by C.F. Engel and D.L. McFadden, Elsevier.

Matzkin, R.L. (1993)  Nonparametric Identification and Estimation of Polychotomous Choice Models,” Journal of Econometrics, Vol. 58.

Matzkin, R.L. (1992)  Nonparametric and Distribution-Free Estimation of the Binary Choice and the Threshold Crossing Models”,   Econometrica, Vol. 60, No. 2, p. 239.

Matzkin, R.L. (1991)Semiparametric Estimation of Monotone and Concave Utility Functions for Polychotomous Choice Models,’’  Econometrica, Vol. 59, No. 5, pp. 1315-1327.

Matzkin, R.L. (1991), “A Nonparametric Maximum Rank Correlation Estimator” in Barnett, J. Powell, and G. Tauchen (eds.) Nonparametric and Semiparametric Methods in Econometrics and Statistics, Cambridge: Cambridge University Press.

Matzkin, R.L. (1990) “Least-concavity and the Distribution-Free Estimation of Nonparametric Concave Functions,”  CFDP #958, Yale University.