- Agresti, A. Categorical data analysis, volume 482. John Wiley & Sons, 2003. Antoniak, C. E. Mixtures of Dirichlet processes with applications to Bayesian nonparametric problems. The Annals of Statistics, 2(6):1152â1174, 1974.
Paper not yet in RePEc: Add citation now
Bauwens, L., Carpantier, J.-F., and Dufays, A. Autoregressive moving average infinite hidden Markov-switching models. Journal of Business & Economic Statistics, 35(2): 162â182, 2017.
- Becg, C. B. and Gray, R. Calculation of polychotomous logistic regression parameters using individualized regressions. Biometrika, 71(1):11â18, 1984.
Paper not yet in RePEc: Add citation now
- Bhadra, A., Datta, J., Polson, N. G., Willard, B., et al. Lasso meets horseshoe: a survey. Statistical Science, 34(3):405â427, 2019.
Paper not yet in RePEc: Add citation now
Bondell, H. D. and Reich, B. J. Simultaneous factor selection and collapsing levels in ANOVA. Biometrics, 65(1):169â177, 2009.
- Brooks, S. P. and Gelman, A. General methods for monitoring convergence of iterative simulations. Journal of computational and graphical statistics, 7(4):434â455, 1998.
Paper not yet in RePEc: Add citation now
- Burgette, L. F., Reiter, J. P., et al. Multiple-shrinkage multinomial probit models with applications to simulating geographies in public use data. Bayesian Analysis, 8(2): 453â478, 2013.
Paper not yet in RePEc: Add citation now
Carson, R. T. and Louviere, J. J. Statistical properties of consideration sets. Journal of Choice Modelling, 13:37â48, 2014.
- Chiong, K. X. and Shum, M. Random projection estimation of discrete-choice models with large choice sets. Management Science, 2018.
Paper not yet in RePEc: Add citation now
- Choosing λ as in (24) controls the prior mode of the number of clusters. Conley et al. (2008) show that a fixed concentration parameter may results in a tight prior on the number of clusters. By putting a prior on the concentration parameter, we can also govern the prior variance around the number of clusters. We specify a prior distribution f(λ) with prior mean equal to the value in (24). To check the dispersion around the prior mode of Lâ , we evaluate the marginal prior probability density function with Monte Carlo integration, f(Lâ ) = Z
Paper not yet in RePEc: Add citation now
Conley, T. G., Hansen, C. B., McCulloch, R. E., and Rossi, P. E. A semi-parametric Bayesian approach to the instrumental variable problem. Journal of Econometrics, 144 (1):276â305, 2008.
Cramer, J. S. and Ridder, G. Pooling states in the multinomial logit model. Journal of Econometrics, 47(2-3):267â272, 1991. Escobar, M. D. and West, M. Bayesian density estimation and inference using mixtures.
- D 0 500 1000 1500 2000 This figure shows the frequency counts for the explanatory control variables. The frequencies represent the number of observations that are coded as 1 in the binary dummies.
Paper not yet in RePEc: Add citation now
- Econometrica: Journal of the econometric society, pages 1219â1240, 1984.
Paper not yet in RePEc: Add citation now
Fiebig, D. G., Keane, M. P., Louviere, J., and Wasi, N. The generalized multinomial logit model: accounting for scale and coefficient heterogeneity. Marketing science, 29 (3):393â421, 2010.
- FruÌhwirth-Schnatter, S. Markov chain Monte Carlo estimation of classical and dynamic switching and mixture models. Journal of the American Statistical Association, 96 (453):194â209, 2001. Gelman, A. and Rubin, D. B. Inference from iterative simulation using multiple sequences.
Paper not yet in RePEc: Add citation now
- Gertheiss, J., Tutz, G., et al. Sparse modeling of categorial explanatory variables. The Annals of Applied Statistics, 4(4):2150â2180, 2010.
Paper not yet in RePEc: Add citation now
- Geweke, J. Evaluating the accuracy of sampling-based approaches to the calculation of posterior moments. In Bayesian Statistics, pages 169â193. University Press, 1992.
Paper not yet in RePEc: Add citation now
Geweke, J. Interpretation and inference in mixture models: Simple MCMC works. Computational Statistics & Data Analysis, 51(7):3529â3550, 2007.
Geweke, J., Gowrisankaran, G., and Town, R. J. Bayesian inference for hospital quality in a selection model. Econometrica, 71(4):1215â1238, 2003.
Geweke, J., Keane, M., and Runkle, D. Alternative computational approaches to inference in the multinomial probit model. The review of economics and statistics, pages 609â632, 1994.
Gneiting, T. and Raftery, A. E. Strictly proper scoring rules, prediction, and estimation. Journal of the American statistical Association, 102(477):359â378, 2007.
- Greenberg, E. Introduction to Bayesian econometrics. Cambridge University Press, 2012.
Paper not yet in RePEc: Add citation now
Greene, W. H. and Hensher, D. A. Does scale heterogeneity across individuals matter? An empirical assessment of alternative logit models. Transportation, 37(3):413â428, 2010. Hausman, J. and McFadden, D. Specification tests for the multinomial logit model.
- Ho, T.-H. and Chong, J.-K. A parsimonious model of stockkeeping-unit choice. Journal of Marketing Research, 40(3):351â365, 2003.
Paper not yet in RePEc: Add citation now
- Ishwaran, H. and James, L. F. Approximate Dirichlet process computing in finite normal mixtures: Smoothing and prior information. Journal of Computational and Graphical Statistics, 11(3):508â532, 2002.
Paper not yet in RePEc: Add citation now
- Ishwaran, H. and Zarepour, M. Markov chain Monte Carlo in approximate Dirichlet and beta two-parameter process hierarchical models. Biometrika, 87(2):371â390, 2000.
Paper not yet in RePEc: Add citation now
Jacobs, B. J., Donkers, B., and Fok, D. Model-based purchase predictions for large assortments. Marketing Science, 35(3):389â404, 2016.
- Johndrow, J., Dunson, D. B., and Lum, K. Diagonal orthant multinomial probit models. In AISTATS, pages 29â38, 2013.
Paper not yet in RePEc: Add citation now
MacLehose, R. F. and Dunson, D. B. Bayesian semiparametric multiple shrinkage. Biometrics, 66(2):455â462, 2010.
Newey, W. K. and West, K. D. A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica, 55(3):703â708, 1987.
- Nibbering, D. Online supplementary appendix to âA high-dimensional multinomial logit modelâ. Journal of Applied Econometrics, 2023.
Paper not yet in RePEc: Add citation now
- Pauger, D., Wagner, H., et al. Bayesian effect fusion for categorical predictors. Bayesian Analysis, 14(2):341â369, 2019.
Paper not yet in RePEc: Add citation now
- Second, we test for convergence of the sampler by the Geweke (1992) t-test for the null hypothesis of equality of the means computed from the first 20 percent and the last 40 percent of the sample draws. We compute the variances of the means using the Newey and West (1987) heteroskedasticity and autocorrelation robust variance estimator with a bandwidth of four percent of the sample sizes. We reject for 10.3%, 5.6%, and 1.2% of the 960 estimated parameters the null-hypothesis, on a significance level of 10%, 5%, and 1% respectively.
Paper not yet in RePEc: Add citation now
- Sethuraman, J. A constructive definition of Dirichlet priors. Statistica Sinica, 4:639â650, 1994.
Paper not yet in RePEc: Add citation now
- Tibshirani, R. Regression shrinkage and selection via the Lasso. Journal of the Royal Statistical Society. Series B (Methodological), pages 267â288, 1996.
Paper not yet in RePEc: Add citation now
Train, K. E. Discrete choice methods with simulation. Cambridge university press, 2009.
- Van den Hauwe, S. Topics in Applied Macroeconometrics. PhD thesis, Erasmus School of Economics, 2015.
Paper not yet in RePEc: Add citation now
Vincent, M. and Hansen, N. R. Sparse group lasso and high dimensional multinomial classification. Computational Statistics & Data Analysis, 71:771â786, 2014.