The three-step generalized methods of moments (GMM) approach of Kapoor, Kelejian and Prucha (2007), which corrects for spatially correlated errors in static panel data models, is extended by introducing fixed effects, a spatial lag, and a one-period lag of the dependent variable as additional...

We extend the three-step generalized methods of moments (GMM) approach of Kapoor et al. (2007), which corrects for spatially correlated errors in static panel data models, by introducing a spatial lag and a one-period lag of the dependent variable as additional explanatory variables. Combining...

This paper extends an existing outlier-robust estimator of linear dynamic panel data models with fixed effects, which is based on the median ratio of two consecutive pairs of first-differenced data. To improve its precision and robust properties, a general procedure based on many pairwise...

The panel-data regression models are frequently applied to micro-level data, which often suffer from data contamination, erroneous observations, or unobserved heterogeneity. Despite the adverse effects of outliers on classical estimation methods, there are only a few robust estimation methods...

Abstract: The spatial survival models typically impose frailties, which characterize unobserved heterogeneity, to be spatially correlated. This specification relies highly on a pre-determinate covariance structure of the errors. However, the spatial effect may not only exist in the unobserved...

Abstract: The identification of parameters in a nonseparable single-index models with correlated random effects is considered in the context of panel data with a fixed number of time periods. The identification assumption is based on the correlated random-effect structure: the distribution of...