Parameter estimation in spatial econometric models with non-random missing data

被引：2

作者：

Seya, Hajime ^{[1
]}

Tomari, Masashi ^{[2
]}

Uno, Shohei ^{[1
]}

机构：

[1] Kobe Univ, Grad Sch Engn Fac Engn, Kobe, Hyogo, Japan

[2] Nippon Koei Co Ltd, Osaka, Japan

来源：

APPLIED ECONOMICS LETTERS | 2021年 / 28卷 / 06期

关键词：

Sample selection; spatial lag model (SLM); spatial autocorrelation; social interaction; Bayesian Markov chain Monte Carlo (MCMC); SAMPLE SELECTION;

D O I：

10.1080/13504851.2020.1758618

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

This study examines the problem of parameter estimation in spatial econometric/social interaction models with non-random missing outcome data. First, we construct a sample selection model considering spatial lag (autoregressive) dependence. Then, we suggest a parameter estimation method for this model by slightly modifying the Bayesian Markov chain Monte Carlo algorithm proposed in an existing study. A simple illustration indicates that the proposed parameter estimation method performs well overall if the spatial autocorrelation is moderate (spatial parameter equals 0.5 or less), even under a relatively high missing data ratio (around 40%).

引用

页码：440 / 446

页数：7

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