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.
Papers
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.