Spatial and Spatio-temporal Engle-Granger representations, Networks and Common Correlated Effects (original) (raw)

Spatial dynamic panel data models with random effects

2012

We develop a general space–time filter applied to panel data models in order to control for heterogeneity as well as both time and spatial dependence. Treatment of initial period observations is analyzed when the number of time periods is small. A second issue relates to a restriction implied by the filter specification on the space–time cross-product term that can greatly

Generalized Moments Estimation of a Spatially Correlated Panel Data Model

1999

This paper considers estimation of a panel data model with disturbances that are autocorrelated across cross sectional units. It is assumed that the disturbances are spatially correlated, based on some geographic or economic proximity measure. If the time dimension of the data is large, feasible and efficient estimation proceeds by random effects. For the case where the time dimension is small (the usual panel data case), we develop a generalized moments estimation approach that is a generalization of a cross sectional model due to Kelejian and Prucha (1999). We apply this approach in a stochastic frontier framework to a panel of Indonesian rice farms where spatial correlations are based on geographic proximity, altitude and weather. The correlations represent productivity shock spillovers across the rice farms in different villages on the island of Java. Using a Moran I test statistic, we demonstrate empirically that productivity shock spillovers may exist in this (and perhaps othe...

A Solution for Absent Spatial Data: The Common Correlated Effects Estimator

International Regional Science Review, 2020

Informed regional policy needs good regional data. As regional data series for key economic variables are generally absent whereas national-level time series data for the same variables are ubiquitous, we suggest an approach that leverages this advantage. We hypothesize the existence of a pervasive “common factor” represented by the national time series that affects regions differentially. We provide an empirical illustration in which national FDI is used in place of panel data for FDI, which are absent. The proposed methodology is tested empirically with respect to the determinants of regional demand for housing. We use a quasi-experimental approach to compare the results of a “common correlated effects” (CCE) estimator with a benchmark case when absent regional data are omitted. Using three common factors relating to national population, income and housing stock, we find mixed support for the common correlated effects hypothesis. We conclude by discussing how our experimental desi...