spatial regression and econometrics

AM-33 - Spatial filtering
  • Identify modeling situations where spatial filtering might not be appropriate
  • Demonstrate how spatial autocorrelation can be “removed” by resampling
  • Explain how dissolving clusters of blocks with similar values may resolve the spatial correlation problem
  • Explain how the Getis and Tiefelsdorf-Griffith spatial filtering techniques incorporate spatial component variables into OLS regression analysis in order to remedy misspecification and the problem of spatially auto-correlated residuals
  • Explain how spatial correlation can result as a side effect of the spatial aggregation in a given dataset
  • Describe the relationship between factorial kriging and spatial filtering
AM-31 - Principles of spatial econometrics
  • Explain how spatial dependence and spatial heterogeneity violate the Gauss-Markov assumptions of regression used in traditional econometrics
  • Demonstrate how the spatial weights matrix is fundamental in spatial econometrics models
  • Demonstrate why spatial autocorrelation among regression residuals can be an indication that spatial variables have been omitted from the models
  • Demonstrate how spatially lagged, trend surface, or dummy spatial variables can be used to create the spatial component variables missing in a standard regression analysis
  • Describe the general types of spatial econometric models
AM-33 - Spatial filtering
  • Identify modeling situations where spatial filtering might not be appropriate
  • Demonstrate how spatial autocorrelation can be “removed” by resampling
  • Explain how dissolving clusters of blocks with similar values may resolve the spatial correlation problem
  • Explain how the Getis and Tiefelsdorf-Griffith spatial filtering techniques incorporate spatial component variables into OLS regression analysis in order to remedy misspecification and the problem of spatially auto-correlated residuals
  • Explain how spatial correlation can result as a side effect of the spatial aggregation in a given dataset
  • Describe the relationship between factorial kriging and spatial filtering

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