We review the empirical literature that estimates the causal effect of parent’s schooling on child’s schooling, and conclude that estimates differ across studies. We then consider three explanations for why this is: (a) idiosyncratic differences in data sets; (b) differences in remaining...

This paper studies the decision made by a family to invest in student migration. We propose an empirical structural decision model which reflects the importance of both the return to the investment and the budgetary constraint in the choice of the family. We circumvent the problem of endogeneity...

The performance in finite samples is examined of inference obtained by variants of the Arellano-Bond and the Blundell-Bond GMM estimation techniques for single dynamic panel data models with possibly endogenous regressors and cross-sectional heteroskedasticity. By simulation the effects are...

While coping with nonsphericality of the disturbances, standard GMM suffers from a blind spot for exploiting the most effective instruments when these are obtained directly from unconditional rather than conditional moment assumptions. For instance, standard GMM counteracts that exogenous...

This paper considers testing the hypothesis that errors in a panel data model are weakly Cross-sectionally dependent (CD), using the exponent of cross-sectional dependence introduced recently in Bailey, Kapetanios and Pesaran (2012). It is shown that the implicit null of the CD test depends on...

This paper extends the transformed maximum likelihood approach for estimation of dynamic panel data models by Hsiao, Pesaran, and Tahmiscioglu (2002) to the case where the errors are cross-sectionally heteroskedastic. This extension is not trivial due to the incidental parameters problem that...

We provide a framework for inference in dynamic equilibrium models including financial market data at daily frequency, along with macro series at standard lower frequency. Our formulation of the macro-finance model in continuous-time conveniently accounts for the difference in observation...

This paper proposes a novel regularisation method for the estimation of large covariance matrices, which makes use of insights from the multiple testing literature. The method tests the statistical significance of individual pair-wise correlations and sets to zero those elements that are not...

This paper develops a unified framework for fixed and random effects estimation of higher-order spatial autoregressive panel data models with spatial autoregressive disturbances and heteroskedasticity of unknown form in the idiosyncratic error component. We derive the moment conditions and...

This paper proposes the transformed maximum likelihood estimator for short dynamic panel data models with interactive fixed effects, and provides an extension of Hsiao et al. (2002) that allows for a multifactor error structure. This is an important extension since it retains the advantages of...