SAS Tutorial | Introduction to Spatial Econometric Modeling

Spatial data has become increasingly popular in recent decades and modern data-collection processes often involve recording information about the geographic location of each observation. Among the many disciplines that study spatial data is spatial econometrics, a subfield of econometrics that concentrates on the econometric modeling and analysis of spatial data. In this tutorial, we teach you how the use of spatial data and the application of spatial econometric methods can expand your modeling capabilities and produce more accurate results. We will discuss commonly used spatial econometric models, including spatial autoregressive (SAR), spatial Durbin (SDM), spatial error (SEM), spatial moving average (SMA), spatial autoregressive moving average (SARMA) and several other models available in the SPATIALREG and CSPATIALREG procedures. Using real-world data and examples, we will discuss the interpretability of parameter estimates, spatial predictions, hypothesis testing and model selection along with the challenges posed by big spatial data. A basic understanding of SAS® programming and econometrics is required.

Presenter: Guohui Wu, SAS
Session ID: 5311
Topic: Analytics
Audience: Non-Industry Specific
Role: Actuary, Statistician/Biostatistician
Level: Novice

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