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Research Articles

Automatic variable selection for semiparametric spatial autoregressive model

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Pages 655-675 | Received 27 Aug 2020, Accepted 01 Mar 2023, Published online: 12 Jul 2023

References

  • Ahmad, I., Leelahanon, S., Li, Q. (2005). Efficient estimation of a semiparametric partially linear varying coefficient model. The Annals of Statistics 33(1):258–283. 10.1214/009053604000000931
  • Ai, C., Zhang, Y. (2017). Estimation of partially specified spatial panel data models with fixed-effects. Econometric Reviews 36(1-3):6–22. doi:10.1080/07474938.2015.1113641
  • Anselin, L., Bera, A. K. (1998). Spatial dependence in linear regression models with an introduction to spatial econometrics In Handbook of Applied Economic Statistics. New York: Marcel Dekker, pp. 237–289.
  • Cai, Z., Xiao, Z. (2012). Semiparametric quantile regression estimation in dynamic models with partially varying coefficients. Journal of Econometrics 167(2):413–425. doi:10.1016/j.jeconom.2011.09.025
  • Carroll, R. J., Fan, J., Gijbels, I., Wand, M. P. (1997). Generalized partially linear single-index models. Journal of the American Statistical Association 92(438):477–489. doi:10.1080/01621459.1997.10474001
  • Chen, Y., Wang, Q., Yao, W. (2015). Adaptive estimation for varying coefficient models. Journal of Multivariate Analysis 137:17–31. doi:10.1016/j.jmva.2015.01.017
  • Chu, T., Zhu, J., Wang, H. (2011). Penalized maximum likelihood estimation and variable selection in geostatistics. The Annals of Statistics, 39(5):2607–2625. 10.1214/11-AOS919
  • Du, J., Sun, X., Cao, R., Zhang, Z. (2018). Statistical inference for partially linear additive spatial autoregressive models. Spatial Statistics 25:52–67. doi:10.1016/j.spasta.2018.04.008
  • Efron, B., Hastie, T., Johnstone, I., Tibshirani, R. (2004). Least angle regression (with discussion). The Annals of Statistics, 32(2):407–499. 10.1214/009053604000000067
  • Fan, J., Huang, T. (2005). Profile likelihood inferences on semiparametric varying-coefficient partially linear models. Bernoulli, 11(6):1031–1057. 10.3150/bj/1137421639
  • Fan, J., Li, R. (2001). Variable selection via nonconcave penalized likelihood and its oracle properties. Journal of the American Statistical Association 96(456):1348–1360. doi:10.1198/016214501753382273
  • Feng, S., Xue, L. (2014). Bias-corrected statistical inference for partially linear varying coefficient errors-in-variables models with restricted condition. Annals of the Institute of Statistical Mathematics 66(1):121–140. doi:10.1007/s10463-013-0407-z
  • Gilley, O. W., Pace, R. (1996). On the harrison and rubinfeld data. Journal of Environmental Economics and Management 31(3):403–405. doi:10.1006/jeem.1996.0052
  • Harrison, D., Rubinfeld, D. L. (1978). Hedonic housing prices and the demand for clean air. Journal of Environmental Economics and Management 5(1):81–102. doi:10.1016/0095-0696(78)90006-2
  • Hu, T., Xia, Y. (2012). Adaptive semi-varying coefficient model selection. Statistica Sinica 22(2):575–599. 10.5705/ss.2010.105
  • Jencks, C., Mayer, S. (1990). The social consequences of growing up in a poor neighborhood. In Lynn, L. E., McGeary, M. F. H., eds. Inner-city Poverty in the United States, Washington, DC: National Academy Press, pp. 111–186.
  • Jeong, H., Lee, L-F. (2020). Spatial dynamic models with intertemporal optimization: Specification and estimation. Journal of Econometrics 218(1):82–104. doi:10.1016/j.jeconom.2019.10.012
  • Kai, B., Li, R., Zou, H. (2011). New efficient estimation and variable selection methods for semiparametric varying-coefficient partially linear models. The Annals of Statistics 39(1):305–332. 10.1214/10-AOS842
  • Kelejian, H. H., Prucha, I. R. (1998). A generalized spatial two-stage least squares procedure for estimating a spatial autoregressive model with autoregressive disturbances. The Journal of Real Estate Finance and Economics 17(1):99–121.
  • Kelejian, H. H., Prucha, I. R. (1999). A generalized moments estimator for the autoregressive parameter in a spatial model. International Economic Review 40(2):509–533. doi:10.1111/1468-2354.00027
  • Kelejian, H. H., Prucha, I. R. (2010). Specification and estimation of spatial autoregressive models with autoregressive and heteroskedastic disturbances. Journal of Econometrics 157(1):53–67. doi:10.1016/j.jeconom.2009.10.025 20577573
  • Lee, L.-F. (2002). Consistency and efficiency of least squares estimation for mixed regressive, spatial autoregressive models. Econometric Theory 18(2):252–277. doi:10.1017/S0266466602182028
  • Lee, L.-F. (2004). Asymptotic distributions of quasi-maximum likelihood estimators for spatial autoregressive models. Econometrica 72(6):1899–1925. doi:10.1111/j.1468-0262.2004.00558.x
  • Lee, L-F. (2007). GMM AND 2SLS estimation of mixed regressive, spatial autoregressive models. Journal of Econometrics 137(2):489–514. doi:10.1016/j.jeconom.2005.10.004
  • Lee, L-F., Liu, X. (2010). Efficient GMM estimation of high order spatial autoregressive models with autoregressive distrurbances. Econometric Theory 26(1):187–230. doi:10.1017/S0266466609090653
  • Li, T., Guo, Y. (2020). Penalized profile quasi-maximum likelihood method of partially linear spatial autoregressive model. Journal of Statistical Computation and Simulation 90(15):2705–2740. doi:10.1080/00949655.2020.1788561
  • Li, R., Liang, H. (2008). Variable selection in semiparametric regression model. The Annals of Statistics 36(1):261–286. 10.1214/009053607000000604
  • Li, T., Yin, Q., Peng, J. (2020). Variable selection of partially linear varying coefficient spatial autoregressive model. Journal of Statistical Computation and Simulation 90(15):2681–2704. doi:10.1080/00949655.2020.1788560
  • Lian, H. (2012). Semiparametric estimation of additive quantile regression models by two-fold penalty. Journal of Business & Economic Statistics 30(3):337–350. doi:10.1080/07350015.2012.693851
  • Lian, H., Chen, X., Yang, J.-Y. (2012). Identification of partially linear structure in additive models with an application to gene expression prediction from sequences. Biometrics 68(2):437–445. doi:10.1111/j.1541-0420.2011.01672.x 21950383
  • Lian, H., Liang, H., Ruppert, D. (2015). Separation of covariates into nonparametric and parametric parts in high-dimensional partially linear additive models. Statistica Sinica 25(2):591–607.
  • Lin, X., Lee, L-F. (2010). GMM estimation of spatial autoregressive models with unknown heteroskedasticity. Journal of Econometrics 157(1):34–52. doi:10.1016/j.jeconom.2009.10.035
  • Linton, O. (1995). Second order approximation in the partially linear regression model. Econometrica 63(5):1079 doi:10.2307/2171722
  • Liu, X., Chen, J., Cheng, S. (2018). A penalized quasi-maximum likelihood method for variable selection in the spatial autoregressive model. Spatial Statistics 25:86–104. doi:10.1016/j.spasta.2018.05.001
  • Liu, X., Lee, L-F., Bollinger, C. R. (2010). An efficient GMM estimator of spatial autoregressive models. Journal of Econometrics 159(2):303–319. doi:10.1016/j.jeconom.2010.08.001
  • Luo, G., Wu, M. (2022). Statistical inference for semiparametric varying-coefficient spatial autoregressive models under restricted conditions. Communications in Statistics- Simulation and Computation 51(5):2268–2286. doi:10.1080/03610918.2019.1693595
  • Luo, G., Wu, M. (2021). Variable selection for semiparametric varying-coefficient spatial autoregressive models with a diverging number of parameters. Communications in Statistics- Theory and Methods 50(9):2062–2079. doi:10.1080/03610926.2019.1659367
  • Malikov, E., Sun, Y. (2017). Semiparametric estimation and testing of smooth coefficient spatial autoregressive models. Journal of Econometrics 199(1):12–34. doi:10.1016/j.jeconom.2017.02.005
  • Ord, K. (1975). Estimation methods for models of spatial interaction. Journal of the American Statistical Association 70(349):120–126. doi:10.1080/01621459.1975.10480272
  • Pace, R. K., Gilley, O. W. (1997). Using the spatial configuration of the data to improve estimation. The Journal of Real Estate Finance and Economics 14(3):333–340. doi:10.1023/A:1007762613901
  • Paelinck, J. H., Klaassen, L. H. (1979). Spatial Econometrics. Aldershot: Gower Press.
  • Pinkse, J., Slade, M. E., Brett, C. (2002). Spatial price competition: A semiparametric approach. Econometrica 70(3):1111–1153. doi:10.1111/1468-0262.00320
  • Smirnov, O., Anselin, L. (2001). Fast maximum likelihood estimation of very large spatial autoregressive models: A characteristic polynomial approach. Computational Statistics & Data Analysis 35(3):301–319. doi:10.1016/S0167-9473(00)00018-9
  • Su, L. (2012). Semiparametric GMM estimation of spatial autoregressive models. Journal of Econometrics 167(2):543–560. doi:10.1016/j.jeconom.2011.09.034
  • Su, L., Jin, S. (2010). Profile quasi-maximum likelihood estimation of partially linear spatial autoregressive models. Journal of Econometrics 157(1):18–33. doi:10.1016/j.jeconom.2009.10.033
  • Su, L., Yang, Z. (2011). Instrumental variable quantile estimation of spatial autoregressive models. Working paper; Singapore: Management University.
  • Sun, Y., Wu, Y. (2018). Estimation and testing for a partially linear single-index spatial regression model. Spatial Economic Analysis 13(4):473–489. doi:10.1080/17421772.2018.1506150
  • Sun, Y., Zhang, Y., Huang, J. Z. (2019). Estimation of a semiparametric varying-coefficient mixed regressive spatial autoregressive model. Econometrics and Statistics 9:140–155. doi:10.1016/j.ecosta.2017.05.005 30740554
  • Sun, Y. (2016). Functional-coefficient spatial autoregressive models with nonparametric spatial weights. Journal of Econometrics 195(1):134–153. doi:10.1016/j.jeconom.2016.07.005
  • Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Methodological) 58(1):267–288. doi:10.1111/j.2517-6161.1996.tb02080.x
  • Ueki, M. (2009). A note on automatic variable selection using smooth-threshold estimating equations. Biometrika 96(4):1005–1011. doi:10.1093/biomet/asp060
  • Wang, D., Kulasekera, K. B. (2012). Parametric component detection and variable selection in varying-coefficient partially linear models. Journal of Multivariate Analysis 112:117–129. doi:10.1016/j.jmva.2012.05.006
  • Wang, H., Leng, C. (2007). Unified LASSO estimation by least squares approximation. Journal of the American Statistical Association 102(479):1039–1048. doi:10.1198/016214507000000509
  • Wang, H., Xia, Y. (2009). Shrinkage estimation of the varying coefficient model. Journal of the American Statistical Association 104(486):747–757. doi:10.1198/jasa.2009.0138
  • Wang, H. J., Zhu, Z., Zhou, J. (2009). Quantile regression in partially linear varying coefficient models. The Annals of Statistics 37(6B):3841–3866. 10.1214/09-AOS695
  • Wu, Y., Sun, Y. (2017). Shrinkage estimation of the linear model with spatial interaction. Metrika 80(1):51–68. doi:10.1007/s00184-016-0590-z
  • Xia, Y., Zhang, W., Tong, H. (2004). Efficient estimation for semivarying-coefficient models. Biometrika 91(3):661–681. doi:10.1093/biomet/91.3.661
  • Xie, T., Cao, R., Du, J. (2020). Variable selection for spatial autoregressive models with a diverging number of parameters. Statistical Papers 61(3):1125–1145. doi:10.1007/s00362-018-0984-2
  • Yuan, M., Lin, Y. (2006). Model selection and estimation in regression with grouped variables. Journal of the Royal Statistical Society Series B: Statistical Methodology 68(1):49–67. doi:10.1111/j.1467-9868.2005.00532.x
  • Zhang, C. H. (2010). Nearly unbiased variable selection under minimax concave penalty. The Annals of Statistics 38(2):894–942. 10.1214/09-AOS729
  • Zhang, H. H., Cheng, G., Liu, Y. (2011). Linear or nonlinear? automatic structure discovery for partially linear models. Journal of the American Statistical Association 106(495):1099–1112. doi:10.1198/jasa.2011.tm10281 22121305
  • Zhao, W., Zhang, R., Liu, J., Lv, Y. (2014). Robust and efficient variable selection for semiparametric partially linear varying coefficient model based on modal regression. Annals of the Institute of Statistical Mathematics 66(1):165–191. doi:10.1007/s10463-013-0410-4
  • Zhu, J., Huang, H.-C., Reyes, P. E. (2010). On selection of spatial linear models for lattice data. Journal of the Royal Statistical Society Series B: Statistical Methodology 72(3):389–402. doi:10.1111/j.1467-9868.2010.00739.x
  • Zou, H. (2006). The adaptive lasso and its oracle properties. Journal of the American Statistical Association 101(476):1418–1429. doi:10.1198/016214506000000735
  • Zou, H., Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society Series B: Statistical Methodology 67(2):301–320. doi:10.1111/j.1467-9868.2005.00503.x

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