Autoregressive Model With Spatial Dependence and Missing Data

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 PMlevel data is illustrated.

Automated summary

This paper investigates an autoregressive model with spatially correlated error terms and missing data. A logistic regression model is used to model the missingness mechanism, while an autoregressive model accommodates time series dependence and a spatial error model captures spatial dependence. Weighted least squares and weighted maximum likelihood estimators are developed for estimation. Asymptotic properties and finite sample performance are studied, and a real data example on Beijing's PM2.5 data is provided.