Good procedures for formulating quantitative models that are useful in explanation, prediction and decision-making are sorely needed in all sciences and applied areas. Currently, model formulation is more of an art than a science with much disagreement about how to approach the problem. Some emphasize simplicity, parsimony, and Ockham's razor while others emphasize the need for complexity, detail and realism. A number of approaches have been put forward that have been used in practice, namely, method of moments, time-series "identification" procedures using autocovariance matrices, etc., encompassing methods, structural econometric modeling-time series analysis procedures, maximum entropy, quantum statistical inference and so on. Further, the roles of measurement, description, unusual and ugly facts in model formulation have to be considered. Papers presented at the Geisser Conference will consider these difficult issues in detail, present and compare Bayesian and non-Bayesian approaches, and incorporate analyses of data to illustrate the results of applying various methods. A summary of the Conference's research findings will be prepared. Forecasting, prediction and modeling are central theoretical and applied topics in Statistics and Econometrics. Time series forecasting methods that are widely employed in industry and government yield useful forecasts of future developments but little in the way of explanation. On the other hand, causal models can provide predictions and explanations of future developments and how they may be influenced by various policies. Improving procedures for developing, implementing and using forecasting and causal models is a major objective of this Conference. In this connection, a comparative evaluation of Bayesian and non-Bayesian methods for achieving the above objective will be provided using many applications to illustrate general points and evaluate alternative approaches. Thus the Conference will provide evaluations and applications of old and new procedures for developing, implementing and using forecasting and causal models.
