A reliable forecasting model might be used to identify risk factors involved in investment decisions and their relationship to critical outcomes like future default rates. The modeling goal, in this case, is to accurately predict the future. Exploratory data analysis and predictor selection techniques are especially important in these analyses. These models are typically built from an initial set (perhaps empty, perhaps quite large) of potentially relevant predictors. Regression models are also used for quantitative forecasting. A reliable explanatory model might be used to inform planning and policy decisions by identifying factors to be considered in more qualitative analyses. The modeling goal is to achieve a well-specified, accurately calibrated description of important dependencies. Significance tests are especially important in these analyses. Specification analysis compares various extensions and restrictions of the model to evaluate the contributions of individual predictors. The structure of these models is usually suggested by theory. Many regression models in economics are built for explanatory purposes, to understand the interrelationships among relevant economic factors.
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