Let's say you sell a product in Germany, and have observed that there seems to be a correlation between GDP and your product sales. Should you include it in your forecasting model? Well, it depends on the accuracy of the Germany GDP forecast.
The best way to measure forecast accuracy is MASE (Mean Absolute Scaled Error). It was proposed by Rob Hyndman in 2006 and compares forecasts to the naive or the "no change" prediction. The chart below shows MASEs for four different Germany GDP forecasters.
The best way to measure forecast accuracy is MASE (Mean Absolute Scaled Error). It was proposed by Rob Hyndman in 2006 and compares forecasts to the naive or the "no change" prediction. The chart below shows MASEs for four different Germany GDP forecasters.
As we can see, the best forecaster had a MASE of 55%. This is substantially better than a random walk, or simply predicting this month will repeat next month. Ironically, the mot publicized GDP forecaster, the Joint Diagnosis, has the worst record.
Many sales forecasts have MASEs well below 55%. If this is true for your Germany sales forecast, then there is a low probability that your forecasts will improve by adding Germany GDP.
Many sales forecasts have MASEs well below 55%. If this is true for your Germany sales forecast, then there is a low probability that your forecasts will improve by adding Germany GDP.
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