To identify and estimate the effects of spatial autocorrelation on economic outcomes.

To develop statistical models that account for spatial dependence.

To analyze the relationship between economic variables and geographic factors.

To predict the future values of economic variables based on historical data.

Spatial Autoregressive Model (SAR)

Spatial Error Model (SEM)

Spatial Durbin Model (SDM)

Ordinary Least Squares (OLS)

To test for the presence of spatial autocorrelation.

To estimate the magnitude of spatial autocorrelation.

To identify the source of spatial autocorrelation.

To predict the future values of economic variables.

Maximum Likelihood Estimation (MLE)

Generalized Method of Moments (GMM)

Instrumental Variables (IV)

Ordinary Least Squares (OLS)

To define the spatial relationships between observations.

To estimate the magnitude of spatial autocorrelation.

To identify the source of spatial autocorrelation.

To predict the future values of economic variables.

Contiguity matrix

Distance matrix

K-nearest neighbors matrix

Identity matrix

In a spatial lag model, the dependent variable is a function of the lagged values of the independent variables, while in a spatial error model, the error term is a function of the lagged values of the independent variables.

In a spatial lag model, the independent variables are a function of the lagged values of the dependent variable, while in a spatial error model, the error term is a function of the lagged values of the dependent variable.

In a spatial lag model, the dependent variable is a function of the lagged values of the error term, while in a spatial error model, the error term is a function of the lagged values of the error term.

In a spatial lag model, the independent variables are a function of the lagged values of the error term, while in a spatial error model, the error term is a function of the lagged values of the independent variables.

To account for both spatial autocorrelation in the dependent variable and spatial autocorrelation in the independent variables.

To account for spatial autocorrelation in the dependent variable.

To account for spatial autocorrelation in the independent variables.

To account for spatial autocorrelation in the error term.

Moran's I statistic

Geary's C statistic

Ljung-Box test

Breusch-Pagan test

To remove the effects of spatial autocorrelation from the data.

To identify the source of spatial autocorrelation.

To estimate the magnitude of spatial autocorrelation.

To predict the future values of economic variables.

Moving average filter

Inverse distance weighting filter

Kriging filter

Ordinary Least Squares (OLS)

To allow the coefficients of the regression model to vary across space.

To account for spatial autocorrelation in the data.

To identify the source of spatial autocorrelation.

To predict the future values of economic variables.

Local polynomial regression (LPR)

Moving average regression (MAR)

Inverse distance weighting regression (IDWR)

Ordinary Least Squares (OLS)

To account for both spatial autocorrelation and temporal autocorrelation in the data.

To account for spatial autocorrelation in the data.

To account for temporal autocorrelation in the data.

To predict the future values of economic variables.

Spatial autoregressive panel data model (SARPD)

Spatial error panel data model (SEPD)

Spatial Durbin panel data model (SDPD)

Ordinary Least Squares (OLS)