## spatial regression and econometrics

##### 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
##### AM-34 - Spatial expansion and geographically weighted regression
• Perform an analysis using the geographically weighted regression technique
• Discuss the appropriateness of GWR under various conditions
• Describe the characteristics of the spatial expansion method
• Explain the principles of geographically weighted regression
• Compare and contrast GWR with universal kriging using moving neighborhoods
• Explain how allowing the parameters of the model to vary with the spatial location of the sample data can be used to accommodate spatial heterogeneity
• Analyze the number of degrees of freedom in GWR analyses and discuss any possible difficulties with the method based on your results
##### 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-34 - Spatial expansion and geographically weighted regression
• Perform an analysis using the geographically weighted regression technique
• Discuss the appropriateness of GWR under various conditions
• Describe the characteristics of the spatial expansion method
• Explain the principles of geographically weighted regression
• Compare and contrast GWR with universal kriging using moving neighborhoods
• Explain how allowing the parameters of the model to vary with the spatial location of the sample data can be used to accommodate spatial heterogeneity
• Analyze the number of degrees of freedom in GWR analyses and discuss any possible difficulties with the method based on your results
##### 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-34 - Spatial expansion and geographically weighted regression
• Perform an analysis using the geographically weighted regression technique
• Discuss the appropriateness of GWR under various conditions
• Describe the characteristics of the spatial expansion method
• Explain the principles of geographically weighted regression
• Compare and contrast GWR with universal kriging using moving neighborhoods
• Explain how allowing the parameters of the model to vary with the spatial location of the sample data can be used to accommodate spatial heterogeneity
• Analyze the number of degrees of freedom in GWR analyses and discuss any possible difficulties with the method based on your results
##### 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