This paper develops a Mean Group Instrumental Variables (MGIV) estimator for spatial dynamic panel data models with interactive effects, under large N and T asymptotics. Unlike existing approaches that typically impose slope-parameter homogeneity, MGIV accommodates cross-sectional heterogeneity...

The present paper develops a new Instrumental Variables (IV) estimator for spatial, dynamic panel data models with interactive effects under large N and T asymptotics. For this class of models, the only approaches available in the literature are based on quasi-maximum likelihood estimation. The...

This paper puts forward a new instrumental variables (IV) approach for linear panel data models with interactive effects in the error term and regressors. The instruments are transformed regressors and so it is not necessary to search for external instruments. The proposed method asymptotically...

This paper develops a new method for testing for Granger non-causality in panel data models with large cross-sectional (N) and time series (T) dimensions. The method is valid in models with homogeneous or heterogeneous coefficients. The novelty of the proposed approach lies on the fact that...

This paper provides an overview of the existing literature on panel data models with error cross-sectional dependence. We distinguish between spatial dependence and factor structure dependence and we analyse the implications of weak and strong cross-sectional dependence on the properties of the...

This paper proposes a partially heterogeneous framework for the analysis of panel data with fixed T , based on the concept of "partitional clustering". In particular, the population of cross-sectional units is grouped into clusters, such that parameter homogeneity is maintained only within...

This paper provides an overview of the existing literature on panel data models with error cross-sectional dependence. We distinguish between spatial dependence and factor structure dependence and we analyse the implications of weak and strong cross-sectional dependence on the properties of the...

This paper considers the issue of GMM estimation of a short dynamic panel data model when the errors are correlated across individuals. We focus particularly on the conditions required in the cross-sectional dimension of the error process for the dynamic panel GMM estimator to remain consistent....

This paper develops an instrumental variable (IV) estimator for consistent estimation of dynamic panel data models with error cross-sectional dependence when both N and T, the cross-section and time series dimensions respectively, are large. Our approach asymptotically projects out the common...

This paper considers panel data regression models with weakly exogenous or endogenous regressors and residuals generated by a multi-factor error structure. In this case, the standard dynamic panel estimators fail to provide consistent estimates of the parameters. We propose a new estimation...