In this chapter, we study the asymptotic distributions for ordinary least squares (OLS), fully modified OLS (FMOLS), and dynamic OLS (DOLS) estimators in cointegrated regression models in panel data. We show that the OLS, FMOLS, and DOLS estimators are all asymptotically normally distributed....

This chapter uses fully modified OLS principles to develop new methods for estimating and testing hypotheses for cointegrating vectors in dynamic panels in a manner that is consistent with the degree of cross sectional heterogeneity that has been permitted in recent panel unit root and panel...

Partial least squares regression (PLS) is one method to estimate parameters in a linear model when predictor variables are nearly collinear. One way to characterize PLS is in terms of the scaling (shrinkage or expansion) along each eigenvector of the predictor correlation matrix. This...

We use simulation studies (a) to compare Bayesian and likelihood fitting methods, in terms of validity of conclusions, in two-level random-slopes regression (RSR) models, and (b) to compare several Bayesian estimation methods based on Markov chain Monte Carlo, in terms of computational...

Chesher and Jewitt (1987) demonstrated that the Eicker (1963) and White (1980) consistent estimator of the variance-covariance matrix in heteroskedastic models could be severely biased if the design matrix is highly unbalanced. In this paper we, therefore, reconsider Rao's (1970) minimum norm...

In this paper we propose a new approach to the problem of structural change from a Bayesian point of view. Our approach is based on a hierarchical model with a Dirichlet process prior. A notable feature of the Dirichlet process is its discreteness which is useful for detecting structural...