Abstract
The logit model was named by Berkson after probit, its close competitor; the two are the most popular econometric methods used in applied work to estimate models for binary variables. It can be easily extended to the treatment of multinomial variables and enjoys specific properties in panel data binary models. Increasingly flexible logit models have also been elaborated for demand analyses. Their development has been stimulated by the increasing availability of databases on individual discrete choices. Because generalized logit models belong to the class of random utility models, their use has promoted sound applied economic research in demand analysis.
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Bibliography
Amemiya, T. 1985. Advanced econometrics. Cambridge, MA: Harvard University Press.
Andersen, E.B. 1973. Conditional inference and models for measuring. Copenhagen: Mentalhygiejnisk Forlag.
Anderson, S.P., A. de Palma, and J.F. Thisse. 1992. Discrete choice theory of product differentiation. Cambridge, MA: MIT Press.
Arellano, M. 2003. Discrete choices with panel data. Investigaciones Economicas 27: 423–458.
Berkson, J. 1944. Application of the logistic function to bioassay. Journal of the American Statistical Association 39: 357–365.
Berkson, J. 1951. Why I prefer logits to probits. Biometrics 7: 327–339.
Berkson, J. 1955. Maximum likelihood and minimum chi-square estimates of the logistic function. Journal of the American Statistical Association 50: 130–162.
Berry, S.T., J.A. Levinsohn, and A. Pakes. 1995. Automobile prices in market equilibrium. Econometrica 63: 841–890.
Chamberlain, G. 1984. Panel data. In Handbook of econometrics, ed. Z. Griliches and M. Intriligator, Vol. 2. Amsterdam: North-Holland.
Chamberlain, G. 1992. Binary response models for panel data: Identification and information. Cambridge: Harvard University.
Dagsvik, J. 2002. Discrete choice in continuous time: Implications of an intertemporal version of IAA. Econometrica 70: 817–831.
Gouriéroux, C. 2000. Econometrics of qualitative dependent variables. Cambridge: Cambridge University Press.
Gouriéroux, C., A. Monfort, and A. Trognon. 1985. Moindres carrés asymptotiques. Annales de l’INSEE 58: 91–121.
Horowitz, J. 1998. Semiparametric methods in econometrics. Berlin: Springer.
Horowitz, J.L., and N.E. Savin. 2001. Binary response models: Logits, probits and semiparametrics. Journal of Economic Perspectives 15(4): 43–56.
Lancaster, T. 2000. The incidental parameter problem since 1948. Journal of Econometrics 95: 391–413.
Lewbel, A. 2000. Semiparametric qualitative response model estimation with unknown heteroskedasticity or instrumental variables. Journal of Econometrics 97: 145–177.
Luce, R. 1959. Individual choice behavior: A theoretical analysis. New York: Wiley.
Magnac, T. 2004. Panel binary variables and sufficiency: Generalizing conditional logit. Econometrica 72: 1859–1877.
Manski, C.F. 1975. The maximum score estimation of the stochastic utility model of choice. Journal of Econometrics 3: 205–228.
Manski, C.F. 1988. Identification of binary response models. Journal of the American Statistical Association 83: 729–738.
Marschak, J. 1960. Binary choice constraints and random utility indicators. In Mathematical methods in the social sciences, ed. K. Arrow. Stanford: Stanford University Press.
Matzkin, R. 1992. Nonparametric and distribution-free estimation of the binary threshold crossing and the binary choice models. Econometrica 60: 239–270.
McCullagh, P., and J.A. Nelder. 1989. Generalized linear models. London: Chapman and Hall.
McFadden, D. 1974. Conditional logit analysis of qualitative choice behavior. In Frontiers in econometrics, ed. P. Zarembka. New York: Academic Press.
McFadden, D. 1984. Econometric analysis of qualitative response models. In Handbook of econometrics, ed. Z. Griliches and M.D. Intriligator, Vol. 2. Amsterdam: North-Holland.
McFadden, D. 2001. Economic choices. American Economic Review 91: 351–378.
McFadden, D., and K. Train. 2000. Mixed MNL models for discrete responses. Journal of Applied Econometrics 15: 447–470.
Rasch, G. 1960. Probabilistic models for some intelligence and attainment tests. Copenhagen: Denmark Paedagogiske Institut.
Resnick, S.I., and R. Roy. 1991. Random USC functions, max stable process and continuous choice. Annals of Applied Probability 1: 267–292.
Strauss, D. 1992. The many faces of logistic regression. American Statistician 46: 321–327.
Theil, H. 1969. A multinomial extension of the linear logit model. International Economic Review 10: 251–259.
Thurstone, L. 1927. A law of comparative judgement. Psychological Review 34: 273–286.
Train, K. 2003. Discrete choice methods with simulation. Cambridge: Cambridge University Press.
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Magnac, T. (2018). Logit Models of Individual Choice. In: The New Palgrave Dictionary of Economics. Palgrave Macmillan, London. https://doi.org/10.1057/978-1-349-95189-5_2145
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DOI: https://doi.org/10.1057/978-1-349-95189-5_2145
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