OBJECTIVE The limitation of small sample size of functional genomics experiments has made it necessary to integrate DNA microarray experimental data from different sources. However, experimentation noises and biases of different microarray platforms have made integrated data analysis challenging. In this work, we propose an integrative computational framework to identify candidate biomarker gen...

This paper considers estimation and inference in panel vector autoregressions (PVARs) with fixed effects when the time dimension of the panel is finite, and the cross-sectional dimension is large. A Maximum Likelihood (ML) estimator based on a transformed likelihood function is proposed and shown to be consistent and asymptotically normally distributed irrespective of the unit root and cointegr...

This paper considers the estimation methods for dynamic panel data (DPD) models with fixed effects, which suggested in econometric literature, such as least squares (LS) and generalized method of moments (GMM). These methods obtain biased estimators for DPD models. The LS estimator is inconsistent when the time dimension (T) is short regardless of the cross-sectional dimension (N). Although con...

We are interested in making inference on the AR parameter in an AR(1) panel data model when the time-series dimension is …xed and the cross-section dimension is large. We consider a GMM estimator based on the second order moments of the observed variables. It turns out that when the AR parameter equals unity and certain restrictions apply to the other parameters in the model then local identi…c...

By deriving the finite sample biases, this paper shows analytically why the system GMM estimator in dynamic panel data models is less biased than the first differencing or the level estimators even though the former uses more instruments.