## 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-32 - Spatial autoregressive models • Explain Anselin’s typology of spatial autoregressive models
• Demonstrate how the parameters of spatial auto-regressive models can be estimated using univariate and bivariate optimization algorithms for maximizing the likelihood function
• Justify the choice of a particular spatial autoregressive model for a given application
• Implement a maximum likelihood estimation procedure for determining key spatial econometric parameters
• Apply spatial statistic software (e.g., GEODA) to create and estimate an autoregressive model
• Conduct a spatial econometric analysis to test for spatial dependence in the residuals from least-squares models and spatial autoregressive 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-32 - Spatial autoregressive models • Explain Anselin’s typology of spatial autoregressive models
• Demonstrate how the parameters of spatial auto-regressive models can be estimated using univariate and bivariate optimization algorithms for maximizing the likelihood function
• Justify the choice of a particular spatial autoregressive model for a given application
• Implement a maximum likelihood estimation procedure for determining key spatial econometric parameters
• Apply spatial statistic software (e.g., GEODA) to create and estimate an autoregressive model
• Conduct a spatial econometric analysis to test for spatial dependence in the residuals from least-squares models and spatial autoregressive models
##### AM-32 - Spatial autoregressive models • Explain Anselin’s typology of spatial autoregressive models
• Demonstrate how the parameters of spatial auto-regressive models can be estimated using univariate and bivariate optimization algorithms for maximizing the likelihood function
• Justify the choice of a particular spatial autoregressive model for a given application
• Implement a maximum likelihood estimation procedure for determining key spatial econometric parameters
• Apply spatial statistic software (e.g., GEODA) to create and estimate an autoregressive model
• Conduct a spatial econometric analysis to test for spatial dependence in the residuals from least-squares models and spatial autoregressive 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-32 - Spatial autoregressive models • Explain Anselin’s typology of spatial autoregressive models
• Demonstrate how the parameters of spatial auto-regressive models can be estimated using univariate and bivariate optimization algorithms for maximizing the likelihood function
• Justify the choice of a particular spatial autoregressive model for a given application
• Implement a maximum likelihood estimation procedure for determining key spatial econometric parameters
• Apply spatial statistic software (e.g., GEODA) to create and estimate an autoregressive model
• Conduct a spatial econometric analysis to test for spatial dependence in the residuals from least-squares models and spatial autoregressive 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