Social networks and internal migration in China: A spatial autoregressive model

2021 ◽  
Author(s):  
Chuanmin Zhao ◽  
Xi Qu
Keyword(s):  
Social Networks ◽  
Internal Migration ◽  
Autoregressive Model ◽  
Spatial Autoregressive Model ◽  
Spatial Autoregressive

Multivariate spatial autoregressive model for large scale social networks

2020 ◽  
Vol 215 (2) ◽  
pp. 591-606 ◽  
Author(s):  
Xuening Zhu ◽  
Danyang Huang ◽  
Rui Pan ◽  
Hansheng Wang
Keyword(s):  
Social Networks ◽  
Autoregressive Model ◽  
Large Scale ◽  
Spatial Autoregressive Model ◽  
Spatial Autoregressive

scholarly journals Variable Selection for the Spatial Autoregressive Model with Autoregressive Disturbances

Mathematics ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 1448
Author(s):  
Xuan Liu ◽  
Jianbao Chen
Keyword(s):  
Variable Selection ◽  
Autoregressive Model ◽  
Rapid Development ◽  
Heterogeneous Data ◽  
Spatial Effect ◽  
Spatial Effects ◽  
Spatial Autoregressive Model ◽  
Monte Carlo Studies ◽  
Spatial Autoregressive ◽  
Theoretical Results

Along with the rapid development of the geographic information system, high-dimensional spatial heterogeneous data has emerged bringing theoretical and computational challenges to statistical modeling and analysis. As a result, effective dimensionality reduction and spatial effect recognition has become very important. This paper focuses on variable selection in the spatial autoregressive model with autoregressive disturbances (SARAR) which contains a more comprehensive spatial effect. The variable selection procedure is presented by using the so-called penalized quasi-likelihood approach. Under suitable regular conditions, we obtain the rate of convergence and the asymptotic normality of the estimators. The theoretical results ensure that the proposed method can effectively identify spatial effects of dependent variables, find spatial heterogeneity in error terms, reduce the dimension, and estimate unknown parameters simultaneously. Based on step-by-step transformation, a feasible iterative algorithm is developed to realize spatial effect identification, variable selection, and parameter estimation. In the setting of finite samples, Monte Carlo studies and real data analysis demonstrate that the proposed penalized method performs well and is consistent with the theoretical results.

GMM estimation of the spatial autoregressive model in a system of interrelated networks

2018 ◽  
Vol 69 ◽  
pp. 167-198 ◽  
Author(s):  
Wei Wang ◽  
Lung-Fei Lee ◽  
Yan Bao
Keyword(s):  
Autoregressive Model ◽  
Spatial Autoregressive Model ◽  
Gmm Estimation ◽  
Spatial Autoregressive

Robust variable selection with exponential squared loss for the spatial autoregressive model

2021 ◽  
Vol 155 ◽  
pp. 107094
Author(s):  
Yunquan Song ◽  
Xijun Liang ◽  
Yanji Zhu ◽  
Lu Lin
Keyword(s):  
Variable Selection ◽  
Autoregressive Model ◽  
Spatial Autoregressive Model ◽  
Spatial Autoregressive

scholarly journals Estimation of Partially Specified Spatial Autoregressive Model

2014 ◽  
Vol 2 (3) ◽  
pp. 226-235
Author(s):  
Yuanqing Zhang
Keyword(s):  
Autoregressive Model ◽  
Instrumental Variable ◽  
Error Term ◽  
Sufficient Conditions ◽  
Spatial Autoregressive Model ◽  
Dimensional Parameter ◽  
Instrumental Variable Estimation ◽  
Weighting Matrix ◽  
Finite Dimensional ◽  
Spatial Autoregressive

Abstract In this paper, we study estimation of a partially specified spatial autoregressive model with heteroskedasticity error term. Under the assumption of exogenous regressors and exogenous spatial weighting matrix, we propose an instrumental variable estimation. Under some sufficient conditions, we show that the proposed estimator for the finite dimensional parameter is root-n consistent and asymptotically normally distributed and the proposed estimator for the unknown function is consistent and also asymptotically distributed though at a rate slower than root-n. Monte Carlo simulations verify our theory and the results suggest that the proposed method has some practical value.

A Note on Small Sample Properties of Estimators in a First-Order Spatial Autoregressive Model

1982 ◽  
Vol 14 (8) ◽  
pp. 1023-1030 ◽  
Author(s):  
L Anselin
Keyword(s):  
Monte Carlo Simulation ◽  
Autoregressive Model ◽  
Ad Hoc ◽  
Simulation Experiment ◽  
Small Sample ◽  
Bayesian Estimator ◽  
Spatial Autoregressive Model ◽  
First Order ◽  
Small Sample Properties ◽  
Spatial Autoregressive

This note considers a Bayesian estimator and an ad hoc procedure for the parameters of a first-order spatial autoregressive model. The approaches are derived, and their small sample properties compared by means of a Monte Carlo simulation experiment.

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