This paper provides a survey of the existing literature on spatial panel data models. Both static and dynamic models will be considered. The paper also demonstrates that spatial econometric models that include lags of the dependent variable and of the independent variables in both space and time provide a useful tool to quantify the magnitude of direct and indirect effects, both in the short te...

introduction: under the principles of international law, no state has the right to use or permit the use of its territory in such a manner as to cause damage to the environment of other states. spatial econometrics provides a powerful tool to assess the influence of the pollution of neighboring countries on a country's pollution level. spatial spillover effects play a significant role in a...

We "spatialize" residual-based panel cointegration tests for nonstationary spatial panel data in terms of a spatial error correction model (SpECM). Local panel cointegration arises when the data are cointegrated within spatial units but not between them. Spatial panel cointegration arises when the data are cointegrated through spatial lags between spatial units but not within them. Global panel...

Through a geographically weighted panel regression analysis, we demonstrate the spatially varying relationship between weather and corn yields. A balanced panel data of 958 U.S. corn production counties for the period 2002-2006 is used. The results indicate that the relationship between weather and corn yield has large spatial variability. In specific, temperature tends to have negative margina...

Forecasting with Spatial Panel Data This paper compares various forecasts using panel data with spatial error correlation. The true data generating process is assumed to be a simple error component regression model with spatial remainder disturbances of the autoregressive or moving average type. The best linear unbiased predictor is compared with other forecasts ignoring spatial correlation, or...