Advanced SearchSearch Tips
Truncation Parameter Selection in Binary Choice Models
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Truncation Parameter Selection in Binary Choice Models
Kim, Kwang-Rae; Cho, Kyu-Dong; Koo, Ja-Yong;
  PDF(new window)
This paper deals with a density estimation method in binary choice models that can be regarded as a statistical inverse problem. We use an orthogonal basis to estimate density function and consider the choice of an appropriate truncation parameter to reflect the model complexity and the prediction accuracy. We propose a data-dependent rule to choose the truncation parameter in the context of binary choice models. A numerical simulation is provided to illustrate the performance of the proposed method.
Choice Probability;density Estimation;inverse Problem;legendre polynomials;spectral decomposition;MISE;
 Cited by
Athey, S., and Imbens, G.W. (2007). Discrete choice models with multiple unobserved choice characteristics, Preprint.

Bajari, P., Fox, J. and Ryan, S. (2007). Linear regression estimation of discrete choice models with nonparametric distribution of random coefficients, American Economic Review, Papers and Proceedings, 97, 459–463.

Chesher, A. and Santos Silva, J. M. C. (2002). Taste variation in discrete choice models, Review of Economic Studies, 69, 147–168.

Dong, Y. (2010). Endogenous Regressor Binary Choice Models without Instruments, with an Application to Migration, Economics Letters, 107, 33–35.

Efromovich, S. (1999). Nonparametric curve estimation: methods, theory and applications, Springer.

Gautier, E. and Kitamura, Y. (2009). Nonparametric estimation in random coefficients binary choice models, Manuscript.

Groemer, H. (1996). Geometric applications of fourier series and spherical harmonics, Cambridge University Press: Cambridge.

Harding, M. C. and Hausman, J. (2007). Using a laplace approximation to estimate the random coefficients logit model by nonlinear least squares, International Economic Review, 48, 1311–1328.

Healy, D. M., Hendriks, H. and Kim, P. T. (1998). Spherical deconvolution, Journal of Multivariate Analysis, 67, 1–22. crossref(new window)

Healy, D. M. and Kim, P. T. (1996). An empirical Bayes approach to directional data and efficient computation on the sphere, The Annals of Statistics, 24, 232–254.

Huh, J., Kim, P. T., Koo, J.-Y. and Park, J. H. (2004). Directional log-density estimation, Journal of the Korean Statistical Society, 33, 255–269.

Kim, P. T. (1998). Deconvolution density estimation on SO(N). Annals of Statistics, 23, 1083–1102.

Kim, P. T. and Koo, J.-Y. (2000). Directional mixture models and optimal estimation of the mixing density, The Canadian Journal of Statistics, 28, 383–398.

Kim, P. T. and Koo, J.-Y. (2002). Optimal spherical deconvolution, Journal of Multivariate Analysis, 80, 21–42.

Kim, P. T., Koo, J.-Y. and Park, H. J. (2004). Sharp minimaxity and sperical deconvolution for supersmooth error distributions, Journal of Multivariate Analysis, 90, 384–392.

Koo, J.-Y. and Kim, P. T. (2005). Statistical inverse problems on manifolds, The Journal of Fourier Analysis and Applications, 11, 639–653.

Koo, J.-Y. and Kim, P. T. (2008a). Asymptotic minimax bounds for stochastic deconvolution over groups, IEEE Transactions on Information Theory, 54, 289–298.

Koo, J.-Y. and Kim, P. T. (2008b). Sharp adaptation for spherical inverse problems with applications to medical imaging, Journal of Multivariate Analysis, 99, 165–190.

Train, K. E. (2003). Discrete choice methods with simulation, Cambridge University Press: Cambridge.