2018/03/05 by Zhaoxing Gao, Gao, Zhaoxing, Yingying Ma +5 · 2 citations
Economics, Econometrics and Finance · Environmental Science · Mathematics · #Applied mathematics #Artificial intelligence #Autocovariance #Autoregressive integrated moving average #Autoregressive model #Computer science #Covariance #Covariance matrix #Econometrics #Estimator #FOS: Computer and information sciences #Inference #Land Use and Ecosystem Services #Mathematical analysis #Mathematics #Methodology (stat.ME) #Panel data #Regional Economics and Spatial Analysis #STAR model #Spatial and Panel Data Analysis #Statistics #Time series #stat.ME
paper · pdf · doi:10.48550/arxiv.1803.01699
published in arXiv (Cornell University) (Cornell University) · 37 pages, 4 figures
openalex publication_date 2018/03/05 · arxiv created 2018/04/18 · arxiv updated 2018/04/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/08
We propose a new class of spatio-temporal models with unknown and banded autoregressive coefficient matrices. The setting represents a sparse structure for high-dimensional spatial panel dynamic models when panel members represent economic (or other type) individuals at many different locations. The structure is practically meaningful when the order of panel members is arranged appropriately. Note that the implied autocovariance matrices are unlikely to be banded, and therefore, the proposal is radically different from the existing literature on the inference for high-dimensional banded covariance matrices. Due to the innate endogeneity, we apply the least squares method based on a Yule-Walker equation to estimate autoregressive coefficient matrices. The estimators based on multiple Yule-Walker equations are also studied. A ratio-based method for determining the bandwidth of autoregressive matrices is also proposed. Some asymptotic properties of the inference methods are established. The proposed methodology is further illustrated using both simulated and real data sets.