Autoregressive Model With Spatial Dependence and Missing Data

Jing Zhou, Jin Liu, Feifei Wang and Hansheng Wang

Journal of Business & Economic Statistics, 2022, vol. 40, issue 1, 28-34

Abstract: We study herein an autoregressive model with spatially correlated error terms and missing data. A logistic regression model with completely observed covariates is used to model the missingness mechanism. An autoregressive model is used to accommodate time series dependence, and a spatial error model is used to capture spatial dependence. To estimate the model, a weighted least squares estimator is developed for the temporal component, and a weighted maximum likelihood estimator is developed for the spatial component. The asymptotic properties for both estimators are investigated. The finite sample performance is assessed through extensive simulation studies. A real data example about Beijing’s PM2.5 level data is illustrated.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:taf:jnlbes:v:40:y:2022:i:1:p:28-34

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DOI: 10.1080/07350015.2020.1766471

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