One of the largest mistakes I see in quantitative finance is the quick conclusion that a model is the best based on its errors. In time-series, statistics, and data science over fitting a model is a common mistake. In this video I show an example of a model that is not over fitted but that is incorrectly specified. By diffing deeper and really understanding your model, you can avoid making silly mistakes.
Don't judge a model by its errors, judge it by its structure.
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