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)...

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...

Measurement error causes a downward bias when estimating a panel data linear regression model. The panel data context offers various opportunities to derive moment conditions that result in consistent GMM estimators. We consider three sources of moment conditions: (i) restrictions on the...

This paper contributes to the GMM literature by introducing the idea of self-instrumenting target variables instead of searching for instruments that are uncorrelated with the errors, in cases where the correlation between the target variables and the errors can be derived. The advantage of the...

This paper develops a model for dynamic binary choice panel data that allows for unobserved heterogeneity to be arbitrarily correlated with covariates. The model is of the exponential type. We derive moment conditions that enable us to eliminate the unobserved heterogeneity term and at the same...

This paper investigates the long-run effects of public debt and inflation on economic growth. Our contribution is both theoretical and empirical. On the theoretical side, we develop a cross-sectionally augmented distributed lag (CS-DL) approach to the estimation of long-run effects in dynamic...

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...