Project Euclid

References

• Abadie, A., Angrist, J., and Imbens, G. (2002). “Instrumental variable estimates of the effect of subsidized training on the quantiles of trainee earnings.” Econometrica, 70: 91–117.
  Zentralblatt MATH: 1104.62331
  Digital Object Identifier: doi:10.1111/1468-0262.00270
  
• Alhamzawi, R. (2014). “Bayesian elastic net Tobit quantile regression.” Communications in Statistics – Simulation and Computation, DOI:10.1080/03610918.2014.904341.
  Mathematical Reviews (MathSciNet): MR3511838
  Zentralblatt MATH: 1347.62129
  Digital Object Identifier: doi:10.1080/03610918.2014.904341
  
• Alhamzawi, R. and Yu, K. (2015). “Bayesian Tobit quantile regression using g-prior distribution.” Journal of Statistical Simulation and Computation, 85: 2903–2918.
  Mathematical Reviews (MathSciNet): MR3357744
  Digital Object Identifier: doi:10.1080/00949655.2014.945449
  
• Amemiya, T. (1979). “The estimation of a simultaneous equation Tobit model.” International Economic Review, 20: 169–181.
  Zentralblatt MATH: 0408.62097
  Digital Object Identifier: doi:10.2307/2526423
  
• Amemiya, T. (1982). “Two stage least square absolute deviations estimators.” Econometrica, 50: 689–771.
  Zentralblatt MATH: 0493.62098
  Digital Object Identifier: doi:10.2307/1912608
  
• Azzalini, A. and Capitanio, A. (2003). “Distributions generated by perturbation of symmetry with emphasis on a multivariate skew $t$-distribution.” Journal of the Royal Statistical Society – Series B, 65: 367–389.
  Zentralblatt MATH: 1065.62094
  Digital Object Identifier: doi:10.1111/1467-9868.00391
  
• Barndorff-Nielsen, O. E. and Shephard, N. (2001). “Non-Gaussian Ornstein–Uhlenbeck-based models and some of their uses in financial economics.” Journal of the Royal Statistical Society – Series B, 63: 167–241.
  Mathematical Reviews (MathSciNet): MR1841412
  Zentralblatt MATH: 0983.60028
  Digital Object Identifier: doi:10.1111/1467-9868.00282
  
• Bilias, Y., Chen, S., and Ying, Z. (2000). “Simple resampling methods for censored regression quantiles.” Journal of Econometrics, 68: 303–338.
  Zentralblatt MATH: 1076.62567
  Digital Object Identifier: doi:10.1016/S0304-4076(00)00042-7
  
• Blundell, R. W. and Powell, J. L. (2007). “Censored regression quantiles with endogenous regressors.” Journal of Econometrics, 141: 65–83.
  Mathematical Reviews (MathSciNet): MR2411737
  Zentralblatt MATH: 06584646
  Digital Object Identifier: doi:10.1016/j.jeconom.2007.01.016
  
• Blundell, R. W. and Smith, R. J. (1989). “Estimation in a class of simultaneous equation limited dependent variable models.” Review of Economics Studies, 56: 37–57.
  Zentralblatt MATH: 0658.62137
  Digital Object Identifier: doi:10.2307/2297748
  
• Buchinsky, M. (1998). “Recent advances in quantile regression models: a practical guide for empirical research.” Journal of Human Resources, 33: 88–126.
• Buchinsky, M. and Hahn, J. (1998). “An alternative estimator for censored quantile regression.” Econometrica, 66: 653–671.
  Zentralblatt MATH: 1055.62569
  Digital Object Identifier: doi:10.2307/2998578
  
• Chen, S. (2010). “An integrated maximum score estimator for a generalized censored quantile regression model.” Journal of Econometrics, 155: 90–98.
  Mathematical Reviews (MathSciNet): MR2592852
  Zentralblatt MATH: 06606024
  Digital Object Identifier: doi:10.1016/j.jeconom.2009.09.020
  
• Chernozhukov, V., Fernández-Val, I., and Kowalski, A. E. (2015). “Quantile regression with censoring and endogeneity.” Journal of Econometrics, 1: 201–221.
  Mathematical Reviews (MathSciNet): MR3321534
  Zentralblatt MATH: 1331.62461
  Digital Object Identifier: doi:10.1016/j.jeconom.2014.06.017
  
• Chernozhukov, V. and Hansen, C. (2005). “An IV model of quantile treatment effects.” Econometrica, 73: 245–261.
  Zentralblatt MATH: 1152.91706
  Digital Object Identifier: doi:10.1111/j.1468-0262.2005.00570.x
  
• Chernozhukov, V. and Hansen, C. (2006). “Instrumental quantile regression inference for structural and treatment effect models.” Journal of Econometrics, 132: 491–525.
  Zentralblatt MATH: 1337.62353
  Digital Object Identifier: doi:10.1016/j.jeconom.2005.02.009
  
• Chernozhukov, V. and Hansen, C. (2008). “Instrumental variable quantile regression: a inference approach.” Journal of Econometrics, 142: 379–398.
  Zentralblatt MATH: 06589312
  
• Chernozhukov, V. and Hong, H. (2002). “Three-step censored quantile regression and extramarital affairs.” Journal of the American Statistical Association, 155: 872–882.
  Mathematical Reviews (MathSciNet): MR1941416
  Zentralblatt MATH: 1048.62112
  Digital Object Identifier: doi:10.1198/016214502388618663
  
• Chernozhukov, V. and Hong, H. (2003). “An MCMC approach to classical estimation.” Journal of Econometrics, 115: 293–346.
  Zentralblatt MATH: 1043.62022
  Digital Object Identifier: doi:10.1016/S0304-4076(03)00100-3
  
• Chib, S. (2001). “Markov chain Monte Carlo methods: computation and inference.” In Heckman, J. J. and Leamer, E. (eds.), Handbook of Econometrics, volume 5, 3569–3649. Amsterdam: North Holland.
• Conley, T. G., Hansen, C. B., McCulloch, R. E., and Rossi, P. E. (2008). “A semi-parametric Bayesian approach to the instrumental variable problem.” Journal of Econometrics, 144: 276–305.
  Zentralblatt MATH: 06592108
  
• Deb, P., Munkin, K. M., and Trivedi, K. P. (2006). “Bayesian analysis of the two-part model with endogeneity: application to health care expenditure.” Journal of Applied Econometrics, 21: 1081–1099.
• Doornik, J. (2007). Ox: Object Oriented Matrix Programming. London: Timberlake Consultants Press.
• Escobar, M. D. and West, M. (1995). “Bayesian density estimation and inference using mixtures.” Journal of the American Statistical Association, 90: 577–588.
  Zentralblatt MATH: 0826.62021
  Digital Object Identifier: doi:10.1080/01621459.1995.10476550
  
• Frühwirth-Schnatter, S. and Pyne, D. (2010). “Bayesian inference for finite mixtures of univariate and multivariate skew-normal and skew-$t$ distributions.” Biostatistics, 11: 317–336.
• Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., and Rubin, D. B. (2014). Bayesian Data Analysis. Boca Raton: CRC Press, 3rd edition.
  Zentralblatt MATH: 1279.62004
  
• Geweke, J., Gowrisankaran, G., and Town, R. J. (2003). “Bayesian inference for hospital quality in a selection model.” Econometrica, 2003: 1215–1238.
  Mathematical Reviews (MathSciNet): MR1995828
  Zentralblatt MATH: 1153.62313
  Digital Object Identifier: doi:10.1111/1468-0262.00444
  
• Hahn, J. (1995). “Bootstrapping quantile regression estimators.” Econometric Theory, 11: 105–121.
• Heckman, J. J. (1978). “Dummy endogenous variables in a simultaneous equation system.” Econometrica, 46: 931–959.
  Zentralblatt MATH: 0382.62095
  Digital Object Identifier: doi:10.2307/1909757
  
• Hoogerheide, L. F., Kaashoek, J. F., and van Dijk, H. K. (2007a). “On the shape of posterior densities and credible sets in instrumental variable regression models.” Journal of Econometrics, 139: 154–180.
  Zentralblatt MATH: 06578075
  
• Hoogerheide, L. F., Kleibergen, F., and van Dijk, H. K. (2007b). “Natural conjugate priors for the instrumental variables regression model applied to the Angrist-Krueger data.” Journal of Econometrics, 138: 63–103.
  Zentralblatt MATH: 06577506
  
• Ishwaran, H. and James, L. F. (2002). “Approximate Dirichlet process computing in finite normal mixtures: smoothing and prior information.” Journal of Computational and Graphical Statistics, 11: 508–532.
• Ji, Y., Lin, N., and Zhang, B. (2012). “Model selection in binary and Tobit quantile regression using the Gibbs sampler.” Computational Statistics & Data Analysis, 56: 827–839.
  Mathematical Reviews (MathSciNet): MR2888728
  Zentralblatt MATH: 1243.62033
  
• Kim, T. H. and Muller, C. (2004). “Two stage quantile regression when the first stage is based on quantile regression.” Econometrics Journal, 7: 218–231.
  Zentralblatt MATH: 1053.62130
  Digital Object Identifier: doi:10.1111/j.1368-423X.2004.00128.x
  
• Kobayashi, G. (2015). “Skew exponential power stochastic volatility model for analysis of skewness, non-normal tails, quantiles and expectiles.” Computational Statistics, DOI:10.1007/s00180-015-0596-4.
  Zentralblatt MATH: 1342.65041
  Digital Object Identifier: doi:10.1007/s00180-015-0596-4
  
• Kobayashi, G. (2016). “Supplementary Ox codes for “Bayesian endogenous Tobit quantile regression”.” Bayesian Analysis.
  Mathematical Reviews (MathSciNet): MR3597571
  Digital Object Identifier: doi:10.1214/16-BA996
  Project Euclid: euclid.ba/1455559718
  
• Kobayashi, G. and Kozumi, H. (2012). “Bayesian analysis of quantile regression for censored dynamic panel data.” Computational Statistics, 27: 359–380.
  Zentralblatt MATH: 1304.65047
  Digital Object Identifier: doi:10.1007/s00180-011-0263-3
  
• Koenker, R. (2005). Quantile Regression. New York: Cambridge University Press.
  Zentralblatt MATH: 1111.62037
  
• Koenker, R. and Bassett, G. (1978). “Regression quantiles.” Econometrica, 46: 33–50.
  Zentralblatt MATH: 0373.62038
  Digital Object Identifier: doi:10.2307/1913643
  
• Koenker, R. and Machado, J. A. F. (1999). “Goodness of fit and related inference processes for quantile regression.” Journal of the American Statistical Association, 94: 1296–1310.
  Zentralblatt MATH: 0998.62041
  Digital Object Identifier: doi:10.1080/01621459.1999.10473882
  
• Kottas, A. and Gelfand, A. E. (2001). “Bayesian semiparametric median regression modeling.” Journal of the American Statistical Association, 96: 1458–1467.
  Zentralblatt MATH: 1051.62038
  Digital Object Identifier: doi:10.1198/016214501753382363
  
• Kottas, A. and Krnjajić, M. (2009). “Bayesian semiparametric modelling in quantile regression.” Scandinavian Journal of Statistics, 36: 297–319.
  Zentralblatt MATH: 1190.62053
  Digital Object Identifier: doi:10.1111/j.1467-9469.2008.00626.x
  
• Kotz, S., Kozubowski, T. J., and Podgòrski, K. (2001). The Laplace Distribution and Generalizations: A Revisit with Applications to Communications, Economics, Engineering, and Finance. Boston: Birkhäuser.
  Zentralblatt MATH: 0977.62003
  
• Kozumi, H. and Kobayashi, G. (2011). “Gibbs sampling methods for Bayesian quantile regression.” Journal of Statistical Simulation and Computation, 81: 1565–1578.
  Zentralblatt MATH: 06182255
  
• Lancaster, T. and Jun, S. J. (2010). “Bayesian quantile regression methods.” Journal of Applied Econometrics, 25: 287–307.
• Lee, S. (2007). “Endogeneity in quantile regression models: a control function approach.” Journal of Econometrics, 141: 1131–1158.
  Zentralblatt MATH: 06586844
  
• Lin, G., He, X., and Portnoy, S. (2012). “Quantile regression with doubly censored data.” Computational Statistics & Data Analysis, 56: 797–812.
  Mathematical Reviews (MathSciNet): MR2888726
  Zentralblatt MATH: 1243.62056
  
• Lopes, H. F. and Polson, N. G. (2014). “Bayesian instrumental variables: priors and likelihoods.” Econometric Reviews, 33: 100–121.
  Mathematical Reviews (MathSciNet): MR3170842
  Digital Object Identifier: doi:10.1080/07474938.2013.807146
  
• Ma, L. and Koenker, R. (506). “Quantile regression methods for recursive structural equation models.” Journal of Econometrics, 134: 471.
  Zentralblatt MATH: 06575915
  
• Mroz, T. (1987). “The sensitivity of an empirical model of married women’s hours of work to economic and statistical assumptions.” Econometrica, 55: 765–799.
• Munkin, M. K. and Trivedi, P. K. (2003). “Bayesian analysis of a self-selection model with multiple outcomes using simulation-based estimation: an application to the demand for healthcare.” Journal of Econometrics, 114: 197–220.
  Zentralblatt MATH: 1013.62113
  Digital Object Identifier: doi:10.1016/S0304-4076(02)00223-3
  
• Munkin, M. K. and Trivedi, P. K. (2008). “Bayesian analysis of the ordered probit model with endogenous selection.” Journal of Econometrics, 143: 334–348.
  Zentralblatt MATH: 06589677
  
• Naranjo, L., Pérez, C. J., and Martín, J. (2015). “Bayesian analysis of some models that use the asymmetric exponential power distribution.” Statistics and Computing, 25: 497–514.
• Nelson, F. and Olsen, L. (1978). “Specification and estimation of a simultaneous equation model with limited dependent variables.” International Economic Review, 19: 659–705.
• Newey, W. K. (1987). “Efficient estimation of limited dependent variable models with endogenous explanatory variables.” Journal of Econometrics, 36: 231–250.
  Mathematical Reviews (MathSciNet): MR914412
  Zentralblatt MATH: 0635.62113
  Digital Object Identifier: doi:10.1016/0304-4076(87)90001-7
  
• Ogasawara, K. and Kobayashi, G. (2015). “The impact of social workers on infant mortality in inter-war Tokyo: Bayesian dynamic panel quantile regression with endogenous variables.” Cliometrica, 9: 97–130.
• Papaspiliopoulos, O. and Roberts, G. O. (2008). “Retrospective MCMC for Dirichlet process hierarchical models.” Biometrika, 95: 169–186.
  Mathematical Reviews (MathSciNet): MR2409721
  Zentralblatt MATH: 05563385
  Digital Object Identifier: doi:10.1093/biomet/asm086
  
• Powell, J. L. (1983). “The asymptotic normality of two-stage least absolute deviations estimators.” Econometrica, 51: 1569–1576.
  Mathematical Reviews (MathSciNet): MR736058
  Zentralblatt MATH: 0537.62095
  Digital Object Identifier: doi:10.2307/1912290
  
• Powell, J. L. (1984). “Least absolute deviations estimation for the censored regression model.” Journal of Econometrics, 20: 303–325.
  Zentralblatt MATH: 0571.62100
  Digital Object Identifier: doi:10.1016/0304-4076(84)90004-6
  
• Powell, J. L. (1986). “Censored regression quantiles.” Journal of Econometrics, 32: 143–155.
  Zentralblatt MATH: 0605.62139
  Digital Object Identifier: doi:10.1016/0304-4076(86)90016-3
  
• Reich, B. J., Bondell, H. D., and Wang, H. J. (2010). “Flexible Bayesian quantile regression for independent and clustered data.” Biostatistics, 11: 337–352.
• Reich, B. J. and Smith, L. B. (2013). “Bayesian quantile regression for censored data.” Biometrics, 68: 651–660.
  Zentralblatt MATH: 06226543
  
• Rossi, P. E., Allenby, G. M., and McCulloch, R. E. (2005). Bayesian Statistics and Marketing. New York: Wiley.
  Zentralblatt MATH: 1094.62037
  
• Sethuraman, J. (1994). “A constructive definition of Dirichlet priors.” Statistica Sinica, 4: 639–650.
  Zentralblatt MATH: 0823.62007
  
• Smith, R. J. and Blundell, R. W. (1986). “An exogeneity test for a simultaneous equation Tobit model with an application to labor supply.” Econometrica, 56: 679–685.
  Zentralblatt MATH: 0606.62136
  Digital Object Identifier: doi:10.2307/1911314
  
• Sriram, K., Ramamoorthi, R. V., and Ghosh, P. (2013). “Posterior consistency of Bayesian quantile regression based on the misspecified asymmetric Laplace density.” Bayesian Analysis, 8: 479–504.
  Zentralblatt MATH: 1329.62308
  Digital Object Identifier: doi:10.1214/13-BA817
  
• Taddy, A. M. and Kottas, A. (2010). “A Bayesian nonparametric approach to inference for quantile regression.” Journal of the American Statistical Association, 28: 357–369.
  Zentralblatt MATH: 1214.62045
  Digital Object Identifier: doi:10.1198/jbes.2009.07331
  
• Tang, Y., Wang, H. J., He, X., and Zhu, Z. (2012). “An informative subset-based estimator for censored quantile regression.” Test, 21: 635–655.
  Zentralblatt MATH: 1284.62269
  Digital Object Identifier: doi:10.1007/s11749-011-0266-y
  
• V. Chernozhukov, B. M., I. Fernández-Val (2013). “Inference on counterfactual distributions.” Econometrica, 81: 2205–2268.
  Zentralblatt MATH: 1326.62223
  Digital Object Identifier: doi:10.3982/ECTA10582
  
• Walker, S. (2007). “Sampling the Dirichlet mixture model with slices.” Communications in Statistics – Simulation and Computation, 36: 45–54.
  Zentralblatt MATH: 1113.62058
  Digital Object Identifier: doi:10.1080/03610910601096262
  
• Wang, H. J. and Fygenson, M. (2009). “Inference for censored quantile regression models in longitudinal studies.” The Annals of Statistics, 37: 756–781.
  Zentralblatt MATH: 1162.62035
  Digital Object Identifier: doi:10.1214/07-AOS564
  Project Euclid: euclid.aos/1236693149
  
• Wang, H. J. and Wang, L. (2009). “Locally weighted censored quantile regression.” Journal of the American Statistical Association, 104: 1117–128.
• Wichitaksorn, N., Choy, S. T. B., and Gerlach, R. (2014). “A generalized class of skew distributions and associated robust quantile regression models.” The Canadian Journal of Statistics, 42: 579–596.
  Zentralblatt MATH: 1329.62086
  Digital Object Identifier: doi:10.1002/cjs.11228
  
• Xie, S., Wan, A. T. K., and Zhou, Y. (2015). “Quantile regression methods with varying-coefficient models for censored data.” Computational Statistics & Data Analysis, 88: 154–172.
  Mathematical Reviews (MathSciNet): MR3332024
  
• Yu, K., Lu, Z., and Stander, J. (2003). “Quantile regression: applications and current research areas.” Statistician, 52: 331–350.
  Mathematical Reviews (MathSciNet): MR2011179
  Digital Object Identifier: doi:10.1111/1467-9884.00363
  
• Yu, K. and Moyeed, R. A. (2001). “Bayesian quantile regression.” Statistics and Probability Letters, 54: 437–447.
  Zentralblatt MATH: 0983.62017
  Digital Object Identifier: doi:10.1016/S0167-7152(01)00124-9
  
• Yu, K. and Stander, J. (2007). “Bayesian analysis of a Tobit quantile regression model.” Journal of Econometrics, 137: 260–276.
  Zentralblatt MATH: 06576084
  
• Yu, K. and Zhang, J. (2005). “A three-parameter asymmetric Laplace distribution and its extension.” Communications in Statistics – Theory and Methods, 34: 1867–1879.
  Mathematical Reviews (MathSciNet): MR2205240
  Zentralblatt MATH: 1072.62005
  Digital Object Identifier: doi:10.1080/03610920500199018
  
• Zhao, K. and Lian, H. (2015). “Bayesian Tobit quantile regression with single-index models.” Journal of Statistical Simulation and Computation, 85: 1247–1263.
• Zhu, D. and Galbraith, J. W. (2011). “Modeling and forecasting expected shortfall with the generalized asymmetric Student-$t$ and asymmetric exponential power distributions.” Journal of Empirical Finance, 18: 765–778.
• Zhu, D. and Zinde-Walsh, V. (2009). “Properties and estimation of asymmetric exponential power distribution.” Journal of Econometrics, 148: 86–99.
  Zentralblatt MATH: 06600549