Panel Data Models with Spatially Nested Random Effects: (session 1: contributed lectures)

SESSION 1: Contributed lectures; National audience; This paper focuses on panel data models with spatially nested random ef- fects. This specification is useful for panel data applications which exhibit spatial dependence and a nested (hierarchical) structure. We propose to use a generalized moments estimator in the spirit of Kelejian and Prucha (1998, 1999) and Kapoor, Kelejian and Prucha (2007) for estimating the spatial autoregressive parameter and the variance components of the disturbance process. Then a spatial counterpart of the Cochrane-Orcutt transformation is defined to obtain a feasible generalized least squares procedure to estimate the regression parameters. Using Monte Carlo simulations, we show that our estimators perform well in terms of root mean square error compared to the maximum likelihood estimator. The approach is demonstrated using English house price data by district, with districts nested within counties.