MSE versus MAD

MSE versus MAD - error is big to begin with and squaring it...

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MSE versus MAD MSE is an accuracy measure which tries to get rid of the direction of the error made by squaring the deviations (errors). The idea is that error is bad whether you overestimate or underestimate in your forecast. So, to overcome the positive or negative sign of the error, you can square the error to get rid of the sign. Unfortunately, in that process you get a bigger number (especially when the
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Unformatted text preview: error is big to begin with, and squaring it will be a large value). But, one should not take square root of MSE. You just compare MSE for several models used to forecast the same time series, and pick the one that has the least MSE. Another accuracy measure is MAD. It is defined in notes and comments section of your book. Please review it and post any comments. Dr. J....
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This note was uploaded on 11/13/2011 for the course MBA 522 taught by Professor Nabavi during the Spring '08 term at Bellevue.

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