Role of information in classical and Bayesian modelling

Information plays a significant role in a decision-making process. Managerial decisions require applied statistical analysis. It is a common practice to use the method of classical least squares. It was observed that indirect least squares type estimation for structural models yields empirically unstable and highly variable results. In this paper we show that this instability and high variability of estimates stems from the lack of information in classical modelling. We suggest Bayesian modelling that allows decision makers to incorporate more information and yields stable and improved results. We provide empirical results in support of the role of information.