When building a model, less can be more

BVWireIssue #99-2
December 8, 2010

Commenting on a recent WSJ blog post “Economists' Grail: A Post-Crash Model“ Aswath Damodaran (Stern School of Business, NYU) says “there seems to be consensus that conventional economic models did a poor job predicting the magnitude of the last crisis and that we need to do better.”

How?  Professor Damodaran thinks we should do less.In his blog post “Are complex models the answer?” he responds: When faced with more uncertainty, strip the model down to only the basic inputs, minimize the complexity and build the simplest model you can. Take out all but the key variables and reduce detail. I use this principle when valuing companies. The more uncertainty I face, the less detail I have in my valuation, recognizing that my capacity to forecast diminishes with uncertainty and that errors I make on these inputs will magnify as they percolate through the valuation. More good news: if I am going to screw up, at least I will do so with a lot less work!!.”

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