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

Abstract

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 design may serve as a methodological prototype for further tests of CCE as a solution to the absent spatial data problem.

Macroeconomics Meets regional science: How national economic activity is Related to regional economic activity

“…A further difference is that RHOMOLO is calibrated in parts whereas BFX is estimated econometrically. Finally, regional data for capital stocks are generally not available (Beenstock and Felsenstein 2021). BFX implements a proposal (Beenstock, Ben Zeev and Felsenstein 2011), which uses spatial data for building completions in the business sector, measured in square meters, reported by the Ministry of Housing and Construction.…”