Final answer:
The bias of a forecast model can be measured by comparing the predicted values to the actual values. One common measure of bias is the mean error, which is the average difference between the predicted and actual values.
Step-by-step explanation:
The bias of a forecast model, or the propensity of a model to under- or overforecast, is typically measured by comparing the predicted values to the actual values. One common measure of bias is the mean error, which is calculated by taking the average of the differences between the predicted values and the actual values. If the mean error is positive, it indicates that the model tends to overforecast, while a negative mean error suggests that the model tends to underforecast.
For example, let's say an economist creates a model to predict stock market points for the next two weeks. At the end of each day, they record the actual points on the index and compare them to the predicted values. If, on average, the predicted values are higher than the actual values, it suggests that the model has a positive bias and tends to overforecast. On the other hand, if the predicted values are consistently lower than the actual values, it indicates a negative bias and the model tends to underforecast.
Other measures of bias include mean absolute error (MAE) and mean squared error (MSE), which provide additional information about the accuracy of the predictions.