This article examines the use of a two-way, random-effects, model with correlated errors and additional explanatory variables in combining cross-section with time series data. This model has been analyzed from a Bayesian viewpoint. Methods are developed for computing posterior distributions of slope coefficients. The advantage of our approach over sampling theory approaches is briefly discussed. It has been shown how one can obtain reasonable inferences about slope coefficients which are the parameters of interest, in the presence of nonestimable nuisance parameters by judicious use of sample and prior information.