Many panel data have the latent subgroup effect on individuals, and it is important to correctly identify these groups since efficiency of resulting estimators can be improved significantly by pooling information individuals within each group. However, currently assumed parametric semiparametric relationship between response predictors may misspecified, which leads a wrong grouping result, nonp...

In this paper we adapt the recently proposed Dynamic Integration ensemble techniques for regression problems and compare their performance to the base models and to the popular ensemble technique of Stacked Regression. We show that the Dynamic Integration techniques are as effective for regression as Stacked Regression when the base models are simple. In addition, we demonstrate an extension to...

This paper stresses the links that exist between concepts that are used in the theory of model reduction and concepts that arise in the missing data literature. This connection motivates the extension of the missing at random (MAR) and the missing completely at random (MCAR) concepts from a static setting, as introduced by Rubin (1976), to the case of dynamic panel data models. Using this exten...

T.W. Anderson and his collaborators have made seminal contributions to inference with instrumental variables and to dynamic panel data models. We review these contributions and the extensive economic and statistical literature that these contributions spawned. We describe our recent work in these two areas, presenting new approaches to (a) making valid inferences in the presence of weak instrum...

It is well-known that maximum likelihood (ML) estimation of the autoregressive parameter of a dynamic panel data model with fixed effects is inconsistent under fixed time series sample size (T ) and large cross section sample size (N) asymptotics. The estimation bias is particularly relevant in practical applications when T is small and the autoregressive parameter is close to unity. The presen...