NEW VARIABLE SELECTION CRITERIA FOR ECONOMETRIC MODELS AND THEIR APPLICATIONS

Variable selection is one of the most pervasive model selection problems in statistical applications. Often referred to as the problem of subset selection, it arises when one wants to model the relationship between a variable of interest and a subset of potential explanatory variables or predictors, but there is uncertainty about which subset to use. Several papers have dealt with various aspects of the problem, but it appears that the typical regression user has not benefited appreciably. One reason for the lack of resolution of the problem is the fact that it is has not been well defined. Indeed, it is apparent that there is not a single problem but rather several problems for which different answers might be appropriate. The intent of this paper is not to give specific answers but merely to present new variable selection criteria. The variables which optimize the criteria are chosen to be the best variables. We find that the new criteria perform consistently well across a wide variety of variable selection problems. The area of their applications includes, in particular, business process simulation, resource management, economics, stochastic models, operation and production management, supply chain management, logistics, risk analysis, scheduling, forecasting, cost benefit analysis, and financial models. The practical utility of these criteria is demonstrated by numerical examples.