Variable selection for spatial autoregressive models with a diverging number of parameters

Xie, Tianfa; Cao, Ruiyuan; Du, Juan

doi:10.1007/s00362-018-0984-2

Cited by 40 publications

(13 citation statements)

References 24 publications

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“…In addition, let the spatial parameter ρ = −0.5, 0, 0.5, which represents different spatial dependencies. Similar to Xie et al (2020), the weight matrix is set to be W…”

Section: Simulation Studymentioning

confidence: 99%

Bayesian estimation for partially linear varying coefficient spatial autoregressive models

Tian

Xu²,

et al. 2022

Statistics and Its Interface

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We propose a fully Bayesian estimation approach for partially linear varying coefficient spatial autoregressive models on the basis of B-spline approximations of nonparametric components. A computational efficient MCMC method that combines the Gibbs sampler with Metropolis-Hastings algorithm is implemented to simultaneously obtain the Bayesian estimates of unknown parameters, as well as their standard error estimates. Monte Carlo simulations are used to investigate the finite sample performance of the proposed method. Finally, a real data analysis of Boston housing data is used to illustrate the usefulness of the proposed methodology.

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“…In addition, let the spatial parameter ρ = −0.5, 0, 0.5, which represents different spatial dependencies. Similar to Xie et al (2020), the weight matrix is set to be W…”

Section: Simulation Studymentioning

confidence: 99%

Bayesian estimation for partially linear varying coefficient spatial autoregressive models

Tian

Xu²,

et al. 2022

Statistics and Its Interface

Self Cite

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show abstract

“…One computational method of calculating IV estimators is two-stage least squares. Inspired by it, we combine the naive least squares method and instrumental variable together and consider a two stage least squares estimation method, similar to Xie et. al.…”

Section: Spatial Autoregressive Modelmentioning

confidence: 99%

Interquantile Shrinkage in Spatial Quantile Autoregressive Regression models

Dong¹,

Hou²,

Song³

2021

Preprint

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Spatial dependent data frequently occur in many fields such as spatial econometrics and epidemiology. To deal with the dependence of variables and estimate quantile-specific effects by covariates, spatial quantile autoregressive models (SQAR models) are introduced. Conventional quantile regression only focuses on the fitting models but ignores the examination of multiple conditional quantile functions, which provides a comprehensive view of the relationship between the response and covariates. Thus, it is necessary to study the different regression slopes at different quantiles, especially in situations where the quantile coefficients share some common feature. However, traditional Wald multiple tests not only increase the burden of computation but also bring greater FDR. In this paper, we transform the estimation and examination problem into a penalization problem, which estimates the parameters at different quantiles and identifies the interquantile commonality at the same time. To avoid the endogeneity caused by the spatial lag variables in SQAR models, we also introduce instrumental variables before estimation and propose two-stage estimation methods based on fused adaptive LASSO and fused adaptive sup-norm penalty approaches. The oracle properties of the proposed estimation methods are established. Through numerical investigations, it is demonstrated that the

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“…To the best of our knowledge, Liu et al [18] investigated variable selection in the SAR model with independent and identically distributed errors, but their model was not under the situation of a diverging number of parameters and the asymptotic properties they established were not available for high-dimensional data. Xie et al [19] considered the penalized estimation for SAR models with a diverging number of parameters and established the oracle properties; however, their method was available for high-dimensional crosssectional data but not for panel data. Terefore, we consider variable selection for the high-dimensional SARP model with fxed efect, present the penalized estimators, and establish related asymptotic properties thoroughly.…”

Section: Introductionmentioning

confidence: 99%

Variable Selection of High-Dimensional Spatial Autoregressive Panel Models with Fixed Effects

Xia

Zhang

Tian

2023

Journal of Mathematics

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This paper studies the variable selection of high-dimensional spatial autoregressive panel models with fixed effects in which a matrix transformation method is applied to eliminate the fixed effects. Then, a penalized quasi-maximum likelihood is developed for variable selection and parameter estimation in the transformed panel model. Under some regular conditions, the consistency and oracle properties of the proposed estimator are established. Some Monte-Carlo experiments and a real data analysis are conducted to examine the finite sample performance of the proposed variable selection procedure, showing that the proposed variable selection method works satisfactorily.

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Variable selection for spatial autoregressive models with a diverging number of parameters

Cited by 40 publications

References 24 publications

Bayesian estimation for partially linear varying coefficient spatial autoregressive models

Bayesian estimation for partially linear varying coefficient spatial autoregressive models

Interquantile Shrinkage in Spatial Quantile Autoregressive Regression models

Variable Selection of High-Dimensional Spatial Autoregressive Panel Models with Fixed Effects

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