three representative models: spatial cross-sectional, static or dynamic panel models. Monte Carlo results show that the …

panel data models with spatial autoregressive disturbances and heteroskedasticity of unknown form in the idiosyncratic error … heteroskedasticity of unknown form in the idiosyncratic error component. Finally, we derive a robust Hausman-test of the spatial random …

Modeling and estimating autocorrelated discrete data can be challenging. In this paper, we use an autoregressive panel … probit model where the autocorrelation in the discrete variable is driven by the autocorrelation in the latent variable. In …

conditions (with or without a unit root), and error characteristics (homoskedasticity or heteroskedasticity of different forms …

used panel heteroskedasticity and autocorrelation consistent (HAC) variance estimator of the pooled estimator under random …In this paper, we propose a robust approach against heteroskedasticity, error serial correlation and slope … heterogeneity for large linear panel data models. First, we establish the asymptotic validity of the Wald test based on the widely …

The maximum likelihood estimator for the regression coefficients, β, in a panel binary response model with fixed …

This paper evaluates the performance of the Breusch-Pagan LM test and the White test for heteroskedasticity in fixed … differences model for different variance functional forms, degrees of heteroskedasticity and sample sizes. Monte Carlo results … identifying heteroskedasticity than the Breusch-Pagan test, even if we are able to identify the actual functional form of the …

used panel heteroskedasticity and autocorrelation consistent (HAC) variance estimator of the pooled estimator under random …In this paper, we propose a robust approach against heteroskedasticity, error serial correlation and slope … heterogeneity for large linear panel data models. First, we establish the asymptotic validity of the Wald test based on the widely …