## 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-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-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-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-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-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-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-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-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-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