Variable selection for quantile autoregressive model: Bayesian methods versus classical methods

References

• R. Alhamzawi and H.T.M. Ali, Brq: An R package for Bayesian quantile regression, Metron 78 (2020), pp. 313–328.
   Web of Science ®Google Scholar
• R. Alhamzawi, K. Yu, and D.F. Benoit, Bayesian adaptive lasso quantile regression, Stat. Model. 12 (2012), pp. 279–297.
   Web of Science ®Google Scholar
• Y. Cai, J. Stander, and N. Davies, A new Bayesian approach to quantile autoregressive time series model estimation and forecasting, J. Time Ser. Anal. 33 (2012), pp. 684–698.
   Web of Science ®Google Scholar
• Y. Cai, G. Montes-Rojas, and J. Olmo, Quantile double AR time series models for financial returns, J. Forecast. 32 (2013), pp. 551–560.
   Web of Science ®Google Scholar
• J. Fan and R.Z. Li, Variable selection via nonconcave penalized likelihood and its oracle properties, J. Am. Stat. Assoc. 96 (2001), pp. 1348–1360.
   Web of Science ®Google Scholar
• B. Fitzenberger, R. Koenker, and J.A.F. Machado, Economic Applications of Quantile Regression, Physica Verlag, Heidelberg, 2002.
   Google Scholar
• R. Koenker, Quantile regression for longitudinal data, J. Multivar. Anal. 91 (2004), pp. 74–89.
   Web of Science ®Google Scholar
• R. Koenker, Quantiles Regression, Cambridge University Press, New York, 2005.
   Google Scholar
• R. Koenker and G. Bassett, Regression quantiles, Econometrica 46 (1978), pp. 33–50.
   Web of Science ®Google Scholar
• R. Koenker and Z. Xiao, Quantile autoregression, J. Am. Stat. Assoc. 101 (2006), pp. 980–990.
   Web of Science ®Google Scholar
• S. Konstantopoulos, W. Li, S. Miller, and A. van der Ploeg, Using quantile regression to estimate intervention effects beyond the mean, Educ. Psychol. Meas. 79 (2019), pp. 883–910.
   PubMed Web of Science ®Google Scholar
• P. Kostov and S. Davidova, A quantile regression analysis of the effect of farmers attitudes and perceptions on market participation, J. Agric. Econ. 64 (2013), pp. 112–132.
   Web of Science ®Google Scholar
• H. Kozumi and G. Kobayashi, Gibbs sampling methods for bayesian quantile regression, J. Stat. Comput. Simul. 81 (2011), pp. 1565–1578.
   Web of Science ®Google Scholar
• D.G. Li, Y. Li, and C.L. Tsai, Quantile correlations and quantile autoregressive modeling, J. Am. Stat. Assoc. 110 (2015), pp. 246–261.
   Web of Science ®Google Scholar
• H. Li, K. Yang, and D. Wang, A threshold stochastic volatility model with explanatory variables, Stat. Neerl. 73 (2019), pp. 118–138.
   Web of Science ®Google Scholar
• D.G. Li, R.C. Zeng, L.W. Zhang, W.K. Li, and D.G. Li, Conditional quantile estimation for hysteretic autoregressive models, Stat. Sin. 30 (2020), pp. 809–827.
   Web of Science ®Google Scholar
• Y. Li and J. Zhu, L1-norm quantile regressions, J. Comput. Graph. Stat. 17 (2008), pp. 163–185.
   Web of Science ®Google Scholar
• H. Mezali and J.E. Beasley, Quantile regression for index tracking and enhanced indexation, J. Oper. Res. Soc. 64 (2013), pp. 1676–1692.
   Web of Science ®Google Scholar
• Z. Miao, F.R.A. Widagdo, L.H. Dong, and F.R. Li, Prediction of branch growth using quantile regression and mixed-effects models: an example with planted larix olgensis henry trees in northeast China, For. Ecol. Manag. 496 (2021). 119407. doi: 10.1016/j.foreco.2021.119407.
   Web of Science ®Google Scholar
• T.J. Mitchell and J.J. Beauchamp, Bayesian variable selection in linear regression, J. Am. Stat. Assoc. 83 (1988), pp. 1023–1032.
   Web of Science ®Google Scholar
• N.N. Narisetty and X. He, Bayesian variable selection with shrinking and diffusing priors, Ann. Stat. 42 (2014), pp. 789–817.
   Web of Science ®Google Scholar
• T. Park and G. Casella, The Bayesian LASSO, J. Am. Stat. Assoc. 103 (2008), pp. 681–686.
   Web of Science ®Google Scholar
• D.J. Spiegelhalte, N.G. Best, B.P. Carlin, and A.V.D. Linde, Bayesian measures of model complexity and fit, J. R. Stat. Soc. B. 64 (2002), pp. 583–639.
   Google Scholar
• W. Sun, J.G. Ibrahim, and F. Zou, Genomewide multiple-loci mapping in experimental crosses by iterative adaptive penalized regression, Genetics 185 (2010), pp. 349–359.
   PubMed Web of Science ®Google Scholar
• R. Tibshirani, Regression shrinkage and selection via the lasso, J. Royal Stat. Soc. B. 58 (1996), pp. 267–288.
   Google Scholar
• Q. Wang and S.N. Chen, Moment estimation for censored quantile regression, Econom. Rev. 40 (2021), pp. 815–829.
   Web of Science ®Google Scholar
• H. Wang, G. Li, and G. Jiang, Robust regression shrinkage and consistent variable selection through the LAD-LASSO, J. Bus. Econ. Stat. 25 (2007), pp. 347–355.
   Web of Science ®Google Scholar
• Y. Wu and Y. Liu, Variable selection in quantile regression, Stat. Sin. 19 (2009), pp. 801–817.
   Web of Science ®Google Scholar
• R. Xi, Y. Li, and Y. Hu, Bayesian quantile regression based on the empirical likelihood with spike and slab priors, Bayesian Anal. 11 (2016), pp. 821–855.
   Web of Science ®Google Scholar
• K. Yang, X. Ding, and X. Yuan, Bayesian empirical likelihood inference and order shrinkage for autoregressive models, Stat. Pap. 63 (2021), pp. 97–121.
   Web of Science ®Google Scholar
• K. Yang, B. Peng, and X. Dong, Bayesian inference for quantile autoregressive model with explanatory variables, Commun. Stat. Theory Methods. (2021). doi: 10.1080/03610926.2021.1964529.
   Web of Science ®Google Scholar
• K. Yang, Q.Q Zhang, X.Y. Yu, and X. Dong, Bayesian inference for a mixture double autoregressive model, Stat. Neerl. (2022). doi: 10.1111/stan.12281.
   Web of Science ®Google Scholar
• K. Yu and R.A. Moyeed, Bayesian quantile regression, Stat. Probab. Lett. 54 (2001), pp. 437–447.
   Web of Science ®Google Scholar
• C. Zhang, Nearly unbiased variable selection under minimax concave penalty, Ann. Stat. 38 (2010), pp. 894–942.
   Web of Science ®Google Scholar
• Y. Zheng, Q. Zhu, G. Li, and Z. Xiao, Hybrid quantile regression estimation for time series models with conditional heteroscedasticity, J. R. Stat. Soc. B. 80 (2018), pp. 975–993.
   Google Scholar
• Q.Q. Zhu, G.D. Li, and Z.J. Xiao, Quantile estimation of regression models with GARCH-X errors, Stat. Sin. 31 (2021), pp. 1261–1284. doi: 10.5705/ss.202019.0003.
   PubMed Web of Science ®Google Scholar
• H. Zou, The adaptive lasso and its oracle properties, J. Am. Stat. Assoc. 101 (2006), pp. 1418–1429.
   Web of Science ®Google Scholar