This chapter provides a survey of the existing literature on spatial panel data models. Both static, dynamic, and dynamic models with common factors will be considered. Common factors are modeled by time-period fixed effects, cross-sectional averages, or principal components. It is demonstrated 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 term and long term. Direct effects can be used to test the hypothesis as to whether a particular variable has a significant effect on the own dependent variable, and indirect effects to test the hypothesis whether spatial spillovers affect the dependent variable of other units. To illustrate these models, their effects estimates, and the impact of the type of common factors, a demand model for cigarettes is estimated based on panel data from 46 U.S. states over the period 1963 to 1992.
|Title of host publication||Handbook of regional science|
|Editors||M. Fischer, P. Nijkamp|
|Publisher||Springer Berlin / Heidelberg|
|Number of pages||19|
|Publication status||Published - 2021|