TY - JOUR
T1 - A Solution for Absent Spatial Data
T2 - The Common Correlated Effects Estimator
AU - Beenstock, Michael
AU - Felsenstein, Daniel
N1 - Publisher Copyright:
© The Author(s) 2020.
PY - 2021/5
Y1 - 2021/5
N2 - 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.
AB - 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.
KW - absent data
KW - common correlated effects estimator
KW - common effects
KW - experimental design
UR - http://www.scopus.com/inward/record.url?scp=85091323355&partnerID=8YFLogxK
U2 - 10.1177/0160017620959132
DO - 10.1177/0160017620959132
M3 - ???researchoutput.researchoutputtypes.contributiontojournal.article???
AN - SCOPUS:85091323355
SN - 0160-0176
VL - 44
SP - 466
EP - 484
JO - International Regional Science Review
JF - International Regional Science Review
IS - 3-4
ER -