Mean signed deviation

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In statistics, the mean signed difference (MSD),[1] also known as mean signed deviation, mean signed error, or mean bias error[2] is a sample statistic that summarizes how well a set of estimates θ̂i match the quantities θi that they are supposed to estimate. It is one of a number of statistics that can be used to assess an estimation procedure, and it would often be used in conjunction with a sample version of the mean square error.

For example, suppose a linear regression model has been estimated over a sample of data, and is then used to extrapolate predictions of the dependent variable out of sample after the out-of-sample data points have become available. Then θi would be the i-th out-of-sample value of the dependent variable, and θ̂i would be its predicted value. The mean signed deviation is the average value of θ̂i−θi.

Definition

The mean signed difference is derived from a set of n pairs, (θ̂i,θi), where θ̂i is an estimate of the parameter θ in a case where it is known that θ=θi. In many applications, all the quantities θi will share a common value. When applied to forecasting in a time series analysis context, a forecasting procedure might be evaluated using the mean signed difference, with θ̂i being the predicted value of a series at a given lead time and θi being the value of the series eventually observed for that time-point. The mean signed difference is defined to be

MSD⁡(θ̂)=1n∑i=1nθî−θi.

Use Cases

The mean signed difference is often useful when the estimations θî are biased from the true values θi in a certain direction. If the estimator that produces the θî values is unbiased, then MSD⁡(θî)=0. However, if the estimations θî are produced by a biased estimator, then the mean signed difference is a useful tool to understand the direction of the estimator's bias.

See also


References

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