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Discrete Choice Modeling

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Abstract

We detail the basic theory for models of discrete choice. This encompasses methods of estimation and analysis of models with discrete dependent variables. Entry level theory is presented for the practitioner. We then describe a few of the recent, frontier developments in theory and practice.

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References

  • Abramovitz, M. and I. Stegun (1971) Handbook of Mathematical Functions. New York: Dover Press.

    Google Scholar 

  • Abrevaya, J. (1997) The equivalence of two estimators of the fixed effects logit model. Economics Letters 55(1), 41–4.

    Article  Google Scholar 

  • Abrevaya, J. and J. Huang (2005) On the bootstrap of the maximum score estimator. Econometrica 73(4), 1175–204.

    Article  Google Scholar 

  • Akin, J., D. Guilkey and R. Sickles (1979) A random coefficient probit model with an application to a study of migration. Journal of Econometrics 11, 233–46.

    Article  Google Scholar 

  • Albert, J. and S. Chib (1993) Bayesian analysis of binary and polytomous response data. Journal of the American Statistical Association 88, 669–79.

    Article  Google Scholar 

  • Aldrich, J. and F. Nelson (1984) Linear Probability, Logit, and Probit Models. Beverly Hills: Sage Publications.

    Google Scholar 

  • Allenby, G.M. and P.E. Rossi (1999) Marketing models of consumer heterogeneity. Journal of Econometrics 89, 57–78.

    Article  Google Scholar 

  • Allison, P. (2000) Problems with fixed-effects negative binomial models. Manuscript, Department of Sociology, University of Pennsylvania.

    Google Scholar 

  • Allison, P. and R. Waterman (2002) Fixed-effects negative binomial regression models. Manuscript, Department of Sociology, University of Pennsylvania.

    Google Scholar 

  • Amemiya, T. (1985) Advanced Econometrics. Cambridge, Mass.: Harvard University Press.

    Google Scholar 

  • Andersen, E. (1970) Asymptotic properties of conditional maximum likelihood estimators. Journal of the Royal Statistical Society, Series B 32, 283–301.

    Google Scholar 

  • Angrist, J. (2001) Estimation of limited dependent variable models with binary endogenous regressors: simple strategies for empirical practice. Journal of Business and Economic Statistics 19(1), 1–14.

    Article  Google Scholar 

  • Avery, R., L. Hansen, and J. Hotz (1983) Multiperiod probit models and orthogonality condition estimation. International Economic Review 24, 21–35.

    Article  Google Scholar 

  • Beck, N., D. Epstein and S. Jackman (2001) Estimating dynamic time series cross section models with a binary dependent variable. Manuscript, Department of Political Science, University of California, San Diego.

    Google Scholar 

  • Ben-Akiva, M. and S. Lerman (1985) Discrete Choice Analysis. London: MIT Press.

    Google Scholar 

  • Berndt, E., B. Hall, R. Hall and J. Hausman (1974) Estimation and inference in nonlinear structural models. Annals of Economic and Social Measurement 3/4, 653–65.

    Google Scholar 

  • Berry, S., J. Levinsohn and A. Pakes (1995) Automobile prices in market equilibrium. Econometrica 63(4), 841–90.

    Article  Google Scholar 

  • Bertschek, I. and M. Lechner (1998) Convenient estimators for the panel probit model. Journal of Econometrics 87(2), 329–72.

    Article  Google Scholar 

  • Bhat, C. (1995) A heteroscedastic extreme value model of intercity mode choice. Transportation Research 30(1), 16–29.

    Google Scholar 

  • Bhat, C. (1996) Accommodating variations in responsiveness to level-of-service measures in travel mode choice modeling. Department of Civil Engineering, University of Massachusetts, Amherst, Working Paper.

    Google Scholar 

  • Bhat, C. (1999) Quasi-random maximum simulated likelihood estimation of the mixed multinomial logit model. Manuscript, Department of Civil Engineering, University of Texas, Austin.

    Google Scholar 

  • Breusch, T. and A. Pagan (1979) A simple test for heteroscedasticity and random coefficient variation. Econometrica 47, 1287–94.

    Article  Google Scholar 

  • Breusch, T. and A. Pagan (1980) The LM test and its applications to model specification in econometrics. Review of Economic Studies 47, 239–54.

    Article  Google Scholar 

  • Boyes, W., D. Hoffman and S. Low (1989) An econometric analysis of the bank credit scoring problem. Journal of Econometrics 40, 3–14.

    Article  Google Scholar 

  • Butler, J. and P. Chatterjee (1995) Pet econometrics: ownership of cats and dogs. Department of Economics, Vanderbilt University, Working Paper 95-WP1.

    Google Scholar 

  • Butler, J. and P. Chatterjee (1997) Tests of the specification of univariate and bivariate ordered probit. Review of Economics and Statistics 79, 343–7.

    Article  Google Scholar 

  • Butler, J., T. Finegan and J. Siegfried (1998) Does more calculus improve student learning in intermediate micro- and macroeconomic theory? Journal of Applied Econometrics 13(2), 185–202.

    Article  Google Scholar 

  • Butler, J. and R. Moffitt (1982) A computationally efficient quadrature procedure for the one factor multinomial probit model. Econometrica 50, 761–64.

    Article  Google Scholar 

  • Calhoun, C. (1991) Desired and excess fertility in Europe and the United States: indirect estimates from world fertility survey data. European Journal of Population 7, 29–57.

    Article  Google Scholar 

  • Cameron, A. and P. Trivedi (1986) Econometric models based on count data: comparisons and applications of some estimators and tests. Journal of Applied Econometrics 1, 29–54.

    Article  Google Scholar 

  • Cameron, C. and P. Trivedi (1998) Regression Analysis of Count Data. New York: Cambridge University Press.

    Book  Google Scholar 

  • Caudill, S. (1988) An advantage of the linear probability model over probit or logit. Oxford Bulletin of Economics and Statistics 50, 425–7.

    Article  Google Scholar 

  • Cecchetti, S. (1986) The frequency of price adjustment: a study of the newsstand prices of magazines. Journal of Econometrics 31(3), 255–74.

    Article  Google Scholar 

  • Chamberlain, G. (1980) Analysis of covariance with qualitative data. Review of Economic Studies 47, 225–38.

    Article  Google Scholar 

  • Charlier, E., B. Melenberg and A. van Soest (1995) A smoothed maximum score estimator for the binary choice panel data model with an application to labor force participation. Statistica Neerlander 49, 324–43.

    Article  Google Scholar 

  • Chesher, A. and M. Irish (1987) Residual analysis in the grouped data and censored normal linear model. Journal of Econometrics 34, 33–62.

    Article  Google Scholar 

  • Christofides, L., T. Hardin and R. Stengos (2000) On the calculation of marginal effects in the bivariate probit model: corrigendum. Economics Letters 68, 339–40.

    Article  Google Scholar 

  • Christofides, L., T. Stengos and R. Swidinsky (1997) On the calculation of marginal effects in the bivariate probit model. Economics Letters 54(3), 203–8.

    Article  Google Scholar 

  • Contoyannis, C., A. Jones and N. Rice (2004) The dynamics of health in the British Household Panel Survey. Journal of Applied Econometrics 19(4), 473–503.

    Article  Google Scholar 

  • Cramer, J. (1999) Predictive performance of the binary logit model in unbalanced samples. Journal of the Royal Statistical Society, Series D (The Statistician) 48, 85–94.

    Article  Google Scholar 

  • D’Addio, A.C., T. Eriksson and P. Frijters (2003) An analysis of the determinants of job satisfaction when individuals’ baseline satisfaction levels may differ. Center for Applied Microeconometrics, University of Copenhagen, Working Paper 2003–16.

    Google Scholar 

  • Daganzo, C. (1979) The Multinomial Probit Model: The Theory and Its Application to Demand Forecasting. New York: Academic Press.

    Google Scholar 

  • Das M. and A. van Soest (2000) A panel data model for subjective information on household income growth. Journal of Economic Behavior and Organization 40, 409–26.

    Article  Google Scholar 

  • Dempster, A., N. Laird and D. Rubin (1977) Maximum likelihood estimation from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B 39, 1–38.

    Google Scholar 

  • Econometric Software, Inc. (2007) LIMDEP, Version 9.0. Plainview, New York: Econometric Software, Inc.

    Google Scholar 

  • Efron, B. (1978) Regression and ANOVA with zero-one data: measures of residual variation. Journal of the American Statistical Association 73, 113–212.

    Article  Google Scholar 

  • Eisenberg, D. and B. Rowe (2006) The effect of serving in the Vietnam war on smoking behavior later in life. Manuscript, School of Public Health, University of Michigan.

    Google Scholar 

  • Fabbri, D., C. Monfardini and R. Radice (2004) Testing exogeneity in the bivariate probit model: Monte Carlo evidence and an application to health economics. Manuscript, Department of Economics, University of Bologna.

    Google Scholar 

  • Ferrer-i-Carbonel, A. and P. Frijter (2004) The effect of methodology on the determinants of happiness. Economic Journal 114, 715–19.

    Google Scholar 

  • Fernandez, A. and J. Rodriguez-Poo (1997) Estimation and testing in female labor participation models: parametric and semiparametric models. Econometric Reviews 16, 229–48.

    Article  Google Scholar 

  • Freedman, D. (2006) On the so-called “Huber Sandwich Estimator” and “Robust Standard Errors.” American Statistician 60(4), 299–302.

    Article  Google Scholar 

  • Frijters P., J. Haisken-DeNew and M. Shields (2004) The value of reunification in Germany: an analysis of changes in life satisfaction. Journal of Human Resources 39(3), 649–74.

    Article  Google Scholar 

  • Gandelman, N. (2005) Homeownership and gender. Manuscript, Universidad ORT, Uruguay.

    Google Scholar 

  • Gaudry, M. and M. Dagenais (1979) The dogit model. Transportation Research, Series B 13, 105–11.

    Article  Google Scholar 

  • Gerfin, M. (1996) Parametric and semi-parametric estimation of the binary response model. Journal of Applied Econometrics 11, 321–40.

    Article  Google Scholar 

  • Geweke, J. (2005) Contemporary Bayesian Econometrics and Statistics. New York: John Wiley and Sons.

    Book  Google Scholar 

  • Geweke, J., M. Keane and D. Runkle (1994) Alternative computational approaches to inference in the multinomial probit model. Review of Economics and Statistics 76, 609–32.

    Article  Google Scholar 

  • Goldberger, A. (1987) Functional Form and Utility: A Review of Consumer Demand Theory. Boulder, Colo: Westview Press.

    Google Scholar 

  • Gourieroux, C. and A. Monfort (1996) Simulation-Based Methods Econometric Methods. Oxford: Oxford University Press.

    Google Scholar 

  • Greene, W. (1992) A statistical model for credit scoring. Department of Economics, Stern School of Business, New York University, Working Paper 92–29.

    Google Scholar 

  • Greene, W. (1995) Sample selection in the poisson regression model. Working Paper No. EC-95–6, Department of Economics, Stern School of Business, New York University.

    Google Scholar 

  • Greene, W. (1996) Marginal effects in the bivariate probit model. Working Paper No. 96-11, Department of Economics, Stern School of Business, New York University.

    Google Scholar 

  • Greene, W. (1997) FIML estimation of sample selection models for count data. Working Paper No. 97-02, Department of Economics, Stern School of Business, New York University.

    Google Scholar 

  • Greene, W. (1998) Gender economics courses in liberal arts colleges: further results. Journal of Economic Education 29(4), 291–300.

    Article  Google Scholar 

  • Greene, W. (2001) Fixed and random effects in nonlinear models. Working Paper No. EC-01–01, Department of Economics, Stern School of Business, New York University.

    Google Scholar 

  • Greene, W. (2004a) Convenient estimators for the panel probit model. Empirical Economics 29(1), 21–47.

    Article  Google Scholar 

  • Greene, W. (2004b) Fixed effects and bias due to the incidental parameters problem in the Tobit model. Econometric Reviews 23(2), 125–47.

    Article  Google Scholar 

  • Greene, W. (2006) Censored data and truncated distributions. In T. Mills and K. Patterson (eds.), Palgrave Handbook of Econometrics, Volume 1. Basingstoke: Palgrave Macmillan.

    Google Scholar 

  • Greene, W. (2007a) LIMDEP/NLOGIT manual. Plainview, New York: Econometric Software, Inc.

    Google Scholar 

  • Greene, W. (2007b) A method of incorporating sample selection in a nonlinear model. Working Paper No. 07–16, Department of Economics, Stern School of Business, New York University.

    Google Scholar 

  • Greene, W. (2008a) Econometric Analysis (sixth edition). Upper Saddle River: Prentice Hall.

    Google Scholar 

  • Greene, W. (2008b) Functional forms for the negative binomial model for count data. Economics Letters 99, 585–90.

    Article  Google Scholar 

  • Greene, W. and D. Hensher (2006) Accounting for heterogeneity in the variance of unobserved effects in mixed logit models. Transportation Research, B: Methodology 40(1), 75–92.

    Article  Google Scholar 

  • Greene, W., S. Rhine and M. Toussaint-Comeau (2006) The importance of check-cashing businesses to the unbanked: a look at racial/ethnic differences. Review of Economics and Statistics 88(1), 146–57.

    Article  Google Scholar 

  • Groot, W. and H.M. Van den Brink (2003) Firm-related training tracks: a random effects ordered probit model. University of Amsterdam, http://www1.fee.uva.nl/scholar/wp/wp23–01.pdf.

    Google Scholar 

  • Gurmu, S.(1997) Semi-parametric estimationof hurdle regression models with an application to Medicaid utilization. Journal of Applied Econometrics 12(3), 225–42.

    Article  Google Scholar 

  • Hardle, W. and C. Manski (1993) Nonparametric and semiparametric approaches to discrete response analysis. Journal of Econometrics 58, 1–274.

    Article  Google Scholar 

  • Harris, M. and X. Zhao (2007) Modelling tobacco consumption with a zero inflated ordered probit model. School of Business and Economics, Monash University, Working Paper 14/04.

    Google Scholar 

  • Hausman, J., B. Hall and Z. Griliches (1984) Economic models for count data with an application to the patents-R&D relationship. Econometrica 52, 909–38.

    Article  Google Scholar 

  • Heckman, J. (1978) State dependence against the hypothesis of spurious state dependence. Annalse de l’INSEE 30, 227–69.

    Google Scholar 

  • Heckman, J. (1979) Sample selection bias as a specification error. Econometrica 47, 153–61.

    Article  Google Scholar 

  • Heckman, J. (1981a) Statistical models for discrete panel data. In C. Manski and D. McFadden (eds.), Structural Analysis of Discrete Data with Econometric Applications. Cambridge, Mass.: MIT Press.

    Google Scholar 

  • Heckman, J. (1981b) Heterogeneity and state dependence. In S. Rosen (ed.), Studies of Labor Markets. Chicago: University of Chicago Press.

    Google Scholar 

  • Heckman, J. and T. MaCurdy (1981) A life cycle model of female labor supply. Review of Economic Studies 47, 247–83.

    Google Scholar 

  • Heckman, J. and J. Snyder (1997) Linear probability models of the demand for attributes with an empirical application to estimating the preferences of legislators. Rand Journal of Economics 28(0).

    Google Scholar 

  • Hensher, D. and W. Greene (2002) Specification and estimation of the nested logit model: alternative normalizations. Transportation Research B 36, 1–17.

    Article  Google Scholar 

  • Hensher, D. and W. Greene (2003) The mixed logit model: the state of practice. Transportation Research B 30, 133–76.

    Google Scholar 

  • Hensher, D., J. Rose and W. Greene (2005) Applied Choice Analysis. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Honore, B. (2002) Non-linear models with panel data. Institute For Fiscal Studies, CEMMAP, Working Paper CWP13/02.

    Google Scholar 

  • Honore, B. and E. Kyriazidou (2000a) Panel data discrete choice models with lagged dependent variables. Econometrica 68(4), 839–74.

    Article  Google Scholar 

  • Honore, B. and E. Kyriazidou (2000b) Estimation of tobit-type models with individual specific effects. Econometric Reviews 19(3), 341–66.

    Article  Google Scholar 

  • Horowitz, J. (1992) A smoothed maximum score estimator for the binary response model. Econometrica 60, 505–31.

    Article  Google Scholar 

  • Horowitz, J. (1993) Semiparametric Estimation of a work-trip mode choice model. Journal of Econometrics 58, 49–70.

    Article  Google Scholar 

  • Hsiao, C. (1986) Analysis of Panel Data. Cambridge: Cambridge University Press.

    Google Scholar 

  • Hsiao, C. (2003) Analysis of Panel Data (second edition). Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Hujer, R. and H. Schneider (1989) The analysis of labor market mobility using panel data. European Economic Review 33, 530–6.

    Article  Google Scholar 

  • Hunt, G. (2000) Alternative nested logit model structures and the special case of partial degeneracy. Journal of Regional Science 40 (February), 89–113.

    Article  Google Scholar 

  • Hyslop, D. (1999) State dependence, serial correlation, and heterogeneity in labor force participation of married women. Econometrica 67(6), 1255–94.

    Article  Google Scholar 

  • Jain, D.C., N.J. Vilcassim and K.D. Chintagunta (1994) A random-coefficients logit brand-choice model applied to panel data. Journal of Business and Economic Statistics 12, 317–28.

    Google Scholar 

  • Jones, J. and J. Landwehr (1988) Removing heterogeneity bias from logit model estimation. Marketing Science 7(1), 41–59.

    Article  Google Scholar 

  • Kassouf, A. and R. Hoffmann (2006) Work related injuries involving children and adolescents: application of a recursive bivariate probit model. Brazilian Review of Econometrics 26(1), 105–26.

    Google Scholar 

  • Katz, E. (2001) Bias in conditional and unconditional fixed effects logit estimation. Political Analysis 9(4), 379–84.

    Article  Google Scholar 

  • Kay, R. and S. Little (1986) Assessing the fit of the logistic model: a case study of children with hemolytic uremic syndrome. Applied Statistics 35, 16–30.

    Article  Google Scholar 

  • Kiefer, N. (1982) Testing for independence in multivariate probit models. Biometrika 69, 161–6.

    Article  Google Scholar 

  • King, G. (1989) A seemingly unrelated poisson regression model. Sociological Methods and Research 17(3), 235–55.

    Article  Google Scholar 

  • Klein, R. and R. Spady (1993) An efficient semiparametric estimator for discrete choice. Econometrica 61, 387–421.

    Article  Google Scholar 

  • Koop, G. (2003) Bayesian Econometrics. New York: John Wiley and Sons.

    Google Scholar 

  • Krinsky, I. and L. Robb (1986) On approximating the statistical properties of elasticities. Review of Economics and Statistics 68(4), 715–19.

    Article  Google Scholar 

  • Kyriazidou, E. (1997) Estimation of a panel data sample selection model. Econometrica 65(6), 1335–64.

    Article  Google Scholar 

  • Lambert, D. (1992) Zero-inflated Poisson regression with an application to defects in manufacturing. Technometrics 34(1), 1–14.

    Article  Google Scholar 

  • Lancaster, T. (2000) The incidental parameters problem since 1948. Journal of Econometrics 95, 391–414.

    Article  Google Scholar 

  • Lancaster, T. (2004) An Introduction to Modern Bayesian Inference. Oxford: Oxford University Press.

    Google Scholar 

  • Lee, E., J. Lee and D. Eastwood (2003) A two step estimation of consumer adoption of technology based service innovations. Journal of Consumer Affairs 37(2), 37–62.

    Article  Google Scholar 

  • Lerman, S. and C. Manski (1981) On the use of simulated frequencies to approximate choice probabilities. In C. Manski and D. McFadden (eds.), Structural Analysis of Discrete Data with Econometric Applications. Cambridge, Mass.: MIT Press.

    Google Scholar 

  • Lewbel, A. (2000) Semiparametric qualitative response model estimation with unknown heteroscedasticity or instrumental variables. Journal of Econometrics 97(1), 145–77.

    Article  Google Scholar 

  • Li, Q and J. Racine (2007) Nonparametric Econometrics. Princeton: Princeton University Press.

    Google Scholar 

  • Long, S. (1997) Regression Models for Categorical and Limited Dependent Variables. Thousand Oaks, CA: Sage Publications.

    Google Scholar 

  • Maddala, G. (1983) Limited Dependent and Qualitative Variables in Econometrics. New York: Cambridge University Press.

    Book  Google Scholar 

  • Magee, L., J. Burbidge and L. Robb (2000) The correlation between husband’s and wife’s education: Canada. 1971–1996. Social and Economic Dimensions of an Aging Population Research Papers 24, McMaster University.

    Google Scholar 

  • Magnac, T. (1997) State dependence and heterogeneity in youth unemployment histories. INRA and CREST, Paris, Working Paper.

    Google Scholar 

  • Manski, C. (1975) The maximum score estimator of the stochastic utility model of choice. Journal of Econometrics 3, 205–28.

    Article  Google Scholar 

  • Manski, C. (1985) Semiparametric analysis of discrete response: asymptotic properties of the maximum score estimator. Journal of Econometrics 27, 313–33.

    Article  Google Scholar 

  • Manski, C. (1986) Operational characteristics of the maximum score estimator. Journal of Econometrics 32, 85–100.

    Article  Google Scholar 

  • Manski, C. (1987) Semiparametric analysis of the random effects linear model from binary response data. Econometrica 55, 357–62.

    Article  Google Scholar 

  • Manski, C. and S. Lerman (1977) The estimation of choice probabilities from choice based samples. Econometrica 45, 1977–88.

    Article  Google Scholar 

  • Manski, C. and S. Thompson (1986) MSCORE: a program for maximum score estimation of linear quantile regressions from binary response data. Mimeo, Department of Economics, University of Wisconsin, Madison.

    Google Scholar 

  • Matzkin, R. (1993) Nonparametric identification and estimation of polytomous choice models. Journal of Econometrics 58, 137–68.

    Article  Google Scholar 

  • McFadden, D. (1981) Econometric Models of Probabilistic Choice. In C. Manski and D. McFadden (eds.), Structural Analysis of Discrete Data with Econometric Applications. Cambridge, Mass.: MIT Press.

    Google Scholar 

  • McFadden, D. (2001) Economic choices. American Economic Review 93(3), 351–78.

    Article  Google Scholar 

  • McFadden, D. and K. Train (2000) Mixed MNL models for discrete response. Journal of Applied Econometrics 15, 447–70.

    Article  Google Scholar 

  • McQuestion, M. (2000) A bivariate probit analysis of social interaction and treatment effects. Center for Demography and Ecology, University of Wisconsin, Working Paper 2000–05.

    Google Scholar 

  • Mullahy, J. (1986) Specification and testing of some modified count data models. Journal of Econometrics 33, 341–65.

    Article  Google Scholar 

  • Mundlak, Y. (1978) On the pooling of time series and cross sectional data. Econometrica 56, 69–86.

    Article  Google Scholar 

  • Murphy, K. and R. Topel (1985) Estimation and inference in two step econometric models. Journal of Business and Economic Statistics 3, 370–9.

    Google Scholar 

  • Newey, W.(1987) Efficient estimation of limited dependent variable models with endogenous explanatory variables. Journal of Econometrics 36, 231–50.

    Article  Google Scholar 

  • Neyman, J. and E. Scott (1948) Consistent estimates based on partially consistent observations. Econometrica 16, 1–32.

    Article  Google Scholar 

  • Pudney, S. and M. Shields (2000) Gender, race, pay and promotion in the British nursing profession: estimation of a generalized ordered probit model. Journal of Applied Econometrics 15(4), 367–99.

    Article  Google Scholar 

  • Pratt, J. (1981) Concavity of the log likelihood. Journal of the American Statistical Association 76, 103–6.

    Article  Google Scholar 

  • Rabe-Hesketh, S., A. Skrondal and A. Pickles (2005) Maximum likelihood estimation of limited and discrete dependent variable models with nested random effects. Journal of Econometrics 128(2), 301–23.

    Article  Google Scholar 

  • Rasch, G. (1960) Probabilistic Models for Some Intelligence and Attainment Tests. Copenhagen, Denmark: Paedogiska.

    Google Scholar 

  • Revelt, D. and K. Train (1998) Mixed logit with repeated choices: households’ choice of appliance efficiency level. Review of Economics and Statistics 80(4), 647–57.

    Article  Google Scholar 

  • Riphahn, R., A. Wambach and A. Million (2003) Incentive effects in the demand for health care: a bivariate panel count data estimation. Journal of Applied Econometrics 18(4), 387–405.

    Article  Google Scholar 

  • Samuelson, P. (1947) Foundations of Economic Analysis. Boston: Atheneum Press.

    Google Scholar 

  • Sepanski, J. (2000) On a random coefficients probit model. Communications in Statistics — Theory and Methods 29, 2493–2505.

    Article  Google Scholar 

  • Shaw, D. (1988) “On-site samples” regression problems of nonnegative integers, truncation, and endogenous stratification. Journal of Econometrics 37, 211–23.

    Article  Google Scholar 

  • Silva, J. (2001) A score test for non-nested hypotheses with applications to discrete response models. Journal of Applied Econometrics 16(5), 577–98.

    Article  Google Scholar 

  • Stata, Inc. (2006) Stata User’s Guide, Version 9.0. College Station, Texas: Stata Press.

    Google Scholar 

  • Terza, J. (1985) Ordinal probit: a generalization. Communications in Statistics 14, 1–12.

    Google Scholar 

  • Terza, J. (1994) Estimating count data models with endogenous switching: sample selection and endogenous treatment effects. Working paper IPRE 94–14. Department of Economics, Pennsylvania State University.

    Google Scholar 

  • Terza, J. (1998) Estimating count data models with endogenous switching: sample selection and endogenous treatment effects. Journal of Econometrics 84(1), 129–54.

    Article  Google Scholar 

  • Tobias, J. and M. Li (2006) Calculus attainment and grades received in intermediate economic theory. Journal of Applied Econometrics 21(6), 893–6.

    Article  Google Scholar 

  • Train, K. (2003) Discrete Choice Methods with Simulation. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • van Doorslaer, E. and W. Nonneman (1987) Economic incentives in the health care industry: implications for health policy making. Health Policy 7(2), 109–14.

    Article  Google Scholar 

  • Vella, F. and M. Verbeek (1999) Two-step estimation of panel data models with censored endogenous variables and selection bias. Journal of Econometrics 90, 239–63.

    Article  Google Scholar 

  • Vuong, Q. (1989) Likelihood ratio tests for model selection and non-nested hypotheses. Econometrica 57, 307–34.

    Article  Google Scholar 

  • Wagstaff, A. (1993) The demand for health: an empirical reformulation of the Grossman model. Health Economics 2, 189–98.

    Article  Google Scholar 

  • White, H. (1980) A heteroscedasticity-consistent covariance matrix estimator and a direct test for heteroscedasticity. Econometrica 48, 817–38.

    Article  Google Scholar 

  • White, N. and A. Wolaver (2003) Occupation choice, information and migration. Review of Regional Studies 33(2), 142–63.

    Google Scholar 

  • Willis, J. (2006) Magazine prices revisited. Journal of Applied Econometrics 21(3), 337–44.

    Article  Google Scholar 

  • Winkelmann, R. (2003) Econometric Analysis of Count Data (fourth edition). Heidelberg: Springer Verlag.

    Book  Google Scholar 

  • Winkelmann, R. (2004) Health care reform and the number of doctor visits — an econometric analysis. Journal of Applied Econometrics 19(4) 455–72.

    Article  Google Scholar 

  • Wooldridge, J. (1995) Selection corrections for panel data models under conditional mean independence assumptions. Journal of Econometrics 68(1), 115–32.

    Article  Google Scholar 

  • Wooldridge, J. (2002a) Econometric Analysis of Cross Section and Panel Data. Cambridge, Mass.: MIT Press.

    Google Scholar 

  • Wooldridge, J. (2002b) Simple solutions to the initial conditions problem in dynamic nonlinear panel data models with unobserved heterogeneity. CEMMAP, IFS and University College, London, Working Paper CWP18/02.

    Google Scholar 

  • Wooldridge, J. (2005) Simple solutions to the initial conditions problem in dynamic, nonlinear panel data models with unobserved heterogeneity. Journal of Applied Econometrics 20(1), 39–54.

    Article  Google Scholar 

  • Wynand, P. and B. van Praag (1981) The demand for deductibles in private health insurance. Journal of Econometrics 17, 229–52.

    Article  Google Scholar 

  • Zavoina, R. and W. McKelvey (1975) A statistical model for the analysis of ordinal level dependent variables. Journal of Mathematical Sociology, Summer, 103–20.

    Google Scholar 

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© 2009 William Greene

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Greene, W. (2009). Discrete Choice Modeling. In: Mills, T.C., Patterson, K. (eds) Palgrave Handbook of Econometrics. Palgrave Macmillan, London. https://doi.org/10.1057/9780230244405_11

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