Final answer:
The raw deviation is an inadequate measure of forecast accuracy because positive deviations can cancel out negative deviations, large errors can overshadow smaller ones, and it does not help identify the sources of deviation. The correct option is C. Positive deviations cancel out negative deviations.
Step-by-step explanation:
When it comes to the accuracy of forecasts, raw deviation can be misleading. Let’s discuss why this is the case. One of the main reasons the raw deviation is not considered an ideal measure is that positive deviations cancel out negative deviations. When measuring forecast accuracy, you want all errors to be accounted for, whether they are overestimates or underestimates. The raw deviation, which is simply the difference between forecasted values and actual values, can have both positive and negative signs. When these are summed up, an inaccurately small number might result due to the cancellation of oppositely signed deviations.
Moreover, another key point is that large errors can tend to mask the effect of small errors. In a dataset where larger errors exist, they can dominate the mean deviation calculation, making it difficult to discern the impact and frequency of smaller errors that could also be significant in understanding forecast performance.
It is also important to note that using raw deviation does not aid in identifying where errors are occurring, which is critical for improving forecast models. A more useful measure, such as the mean squared error, avoids the cancellation issue and provides a clearer picture of the deviation intensity.
In summary, considering these details, the correct option in the context of the student's question about the poor performance of raw deviation as a measure of accuracy of forecasts is C. Positive deviations cancel out negative deviations.