correlation given heteroskedasticity, as well as a conditional LM test for homoskedasticity given spatial correlation, are also … deerived. These LM tests are compared with marginal LM tests that ignore heteroskedasticity in testing for spatial correlation … conditional LM tests when spatial correlation or heteroskedasticity is present. …

In this paper, we propose a robust approach against heteroskedasticity, error serial correlation and slope … used panel heteroskedasticity and autocorrelation consistent (HAC) variance estimator of the pooled estimator under random …

This paper introduces an estimation procedure for a random effects probit model in presence of heteroskedasticity and a … likelihood ratio test for homoskedasticity. The cases where the heteroskedasticity is due to individual effects or idiosyncratic … heteroskedasticity. Furthermore, the power of the test increases with larger individual and time dimensions. The robustness analysis …

) heteroskedasticity and nonlinearity in the relation between the error-ridden covariate and another, error-free, covariate in the equation …

allowance for heteroskedasticity is generally important. The solution is a Fuller (1977) like estimator and standard errors that … are robust to heteroskedasticity and many instruments. We show that the estimator has finite moments and high asymptotic …) under homoskedasticity, and has much lower bias and dispersion under heteroskedasticity, in nearly all cases considered. …

) heteroskedasticity and nonlinearity in the relation between the error-ridden covariate and another, error-free, covariate in the equation …

This paper introduces an estimation procedure for a random effects probit model in presence of heteroskedasticity and a … likelihood ratio test for homoskedasticity. The cases where the heteroskedasticity is due to individual effects or idiosyncratic … heteroskedasticity. Furthermore, the power of the test increases with larger individual and time dimensions. The robustness analysis …

) heteroskedasticity and nonlinearity in the relation between the error-ridden covariate and another, error-free, covariate in the equation …

homoskedasticity against the alternative of heteroskedasticity in both error components. It contrasts this joint LM test with marginal … LM tests that ignore the heteroskedasticity in one of the error components. Monte Carlo results show that misleading … inference can occur when using marginal rather than joint tests when heteroskedasticity is present in both components. …