Arguably, one of the most important issues in regression modeling applied to risk management is the correct specification of the regression equation. How can we assess the validity of the pre-specified regression model, which will provide the basis of statistical inference and practical decisions? It turns out that statistical inference concerning the linear regression model depends crucially on the “validity” of the underlying statistical assumptions of the model. If the assumed underlying statistical assumptions are invalid the inference based on it will be unreliable. The primary objective of this chapter is to outline the key assumptions of the linear regression model and provide some elementary techniques for validating or refuting these assumptions given a specific data set.
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