In the presence of heteroskedasticity, conventional test statistics based on the ordinary least square estimator lead to incorrect inference results for the linear regression model. Given that heteroskedasticity is common in cross-sectional data, the test statistics based on various forms of...

The conventional model of immigrant earnings does not account for the correlation of outcomes across immigrant ethnic networks. We apply a spatial autoregressive network approach to account for the spill-over effects of migrant ethnic group economic resources and labour market outcomes. We...

In the spatial econometrics literature, spatial error dependence is characterized by spatial autoregressive processes, which relate every observation in the cross-section to any other with distance-decaying intensity: i.e., dependence obeys Tobler's First Law of Geography ('everything is related...

The present paper examines the effect of entrepreneurship capital and spatial productivity spillovers on Greek regional labour productivity during Greek financial crisis. A spatial autoregressive (SAR) dynamic panel data model using the robust Blundell and Bond GMM estimator suggests that...

One important goal of this study is to develop a methodology of inference for a widely used Cliff-Ord type spatial model containing spatial lags in the dependent variable, exogenous variables, and the disturbance terms, while allowing for unknown heteroskedasticity in the innovations. We first...

In this paper we specify a linear Cliff and Ord-type spatial model. The model allows for spatial lags in the dependent variable, the exogenous variables, and disturbances. The innovations in the disturbance process are assumed to be heteroskedastic with an unknown form. We formulate a multi-step...

This paper develops an estimator for higher-order spatial autoregressive panel data error component models with spatial autoregressive disturbances, SARAR(R,S). We derive the moment conditions and optimal weighting matrix without distributional assumptions for a generalized moments (GM)...