TY - JOUR
T1 - Testing for heteroskedasticity and spatial correlation in a random effects panel data model
AU - Baltagi, Badi H.
AU - Song, Seuck Heun
AU - Kwon, Jae Hyeok
N1 - Funding Information:
This work was supported by KOSEF (R01-2006-000-10563-0). We would like to thank the guest editor Manfred M. Fischer and two anonymous referees for helpful comments and suggestions. We dedicate this paper in memory of our colleague and co-author Seuck Heun Song who passed away March, 2008.
PY - 2009/6/15
Y1 - 2009/6/15
N2 - A panel data regression model with heteroskedastic as well as spatially correlated disturbances is considered, and a joint LM test for homoskedasticity and no spatial correlation is derived. In addition, a conditional LM test for no spatial correlation given heteroskedasticity, as well as a conditional LM test for homoskedasticity given spatial correlation, are also derived. These LM tests are compared with marginal LM tests that ignore heteroskedasticity in testing for spatial correlation, or spatial correlation in testing for homoskedasticity. Monte Carlo results show that these LM tests, as well as their LR counterparts, perform well, even for small N and T. However, misleading inferences can occur when using marginal, rather than joint or conditional LM tests when spatial correlation or heteroskedasticity is present.
AB - A panel data regression model with heteroskedastic as well as spatially correlated disturbances is considered, and a joint LM test for homoskedasticity and no spatial correlation is derived. In addition, a conditional LM test for no spatial correlation given heteroskedasticity, as well as a conditional LM test for homoskedasticity given spatial correlation, are also derived. These LM tests are compared with marginal LM tests that ignore heteroskedasticity in testing for spatial correlation, or spatial correlation in testing for homoskedasticity. Monte Carlo results show that these LM tests, as well as their LR counterparts, perform well, even for small N and T. However, misleading inferences can occur when using marginal, rather than joint or conditional LM tests when spatial correlation or heteroskedasticity is present.
UR - http://www.scopus.com/inward/record.url?scp=62849099511&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=62849099511&partnerID=8YFLogxK
U2 - 10.1016/j.csda.2008.06.009
DO - 10.1016/j.csda.2008.06.009
M3 - Article
AN - SCOPUS:62849099511
SN - 0167-9473
VL - 53
SP - 2897
EP - 2922
JO - Computational Statistics and Data Analysis
JF - Computational Statistics and Data Analysis
IS - 8
ER -