This paper develops a nonlinear spatial dynamic panel data model, with one particularly interesting application to a structural interaction model for share data. To account for effects from popular units, the spatial weights matrices in our model can allow for dominant units, with unbounded...

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...

I consider a panel vector-autoregressive model with cross-sectional dependence of the disturbances characterized by a spatial autoregressive process. I propose a three-step estimation procedure. Its first step is an instrumental variable estimation that ignores the spatial correlation. In the...

Simultaneity represents a fundamental problem when estimating the elasticity of substitution between capital and labour. To overcome this problem, a wide variety of external instruments has been applied in the literature. However, the use of instruments may lead to wrong inference if they are...

In this paper, we examine the use of Box-Tiao's (1977) canonical correlation method as an alternative to likelihood-based inferences for vector error-correction models. It is now well-known that testing of cointegration ranks based on Johansen's (1995) ML-based method suffers from severe small...

I consider a simultaneous spatial panel data model, jointly modeling three effects: simultaneous effects, spatial effects and common shock effects. This joint modeling and consideration of cross-sectional heteroskedasticity result in a large number of incidental parameters. I propose two...

The problem of instrument proliferation and its consequences (overfitting of the endogenous explanatory variables, biased IV and GMM estimators, weakening of the power of the overidentification tests) are well known. This paper introduces a statistical method to reduce the instrument count. The...

This paper develops estimators for simultaneous equations with spatial auto-regressive or spatial moving average error components. We derive a limited information estimator and a full information estimator. We give the simultaneous generalized method of moments to get each component of the...

The problem of instrument proliferation and its consequences (overfitting of the endogenous explanatory variables, biased IV and GMM estimators, weakening of the power of the overidentification tests) are well known. This paper introduces a statistical method to reduce the instrument count. The...

The problem of instrument proliferation and its consequences (overfitting of endogenous variables, bias of estimates, weakening of Sargan/Hansen test) are well known. The literature provides little guidance on how many instruments is too many. It is common practice to report the instrument count...