What indicates effects below the trend estimate in a time series model?

In a time series model, the trend estimate serves as a baseline or reference point for understanding how actual data points compare over time. When we talk about values that indicate effects below this trend estimate, we are primarily looking at numerical comparisons where actual data points fail to meet or exceed the expected trend.

Values below 1.00 suggest that the actual performance or observations are underperforming relative to the expected trend. This is commonly seen in a ratio analysis, where a value of 1.00 would imply that the data point meets the trend exactly, while values below this threshold indicate a negative deviation from the trend. Thus, a value of 0.90, for example, would signal a performance that is 90% of the predicted trend, thereby falling below the trend estimate.

Higher values, equal to 1, or zero values do not precisely indicate sub-trend performance in the same manner. Higher values signify performance meeting or exceeding the trend, while zero would indicate no activity or performance at all but does not necessarily reflect whether the performance is below the trend. Negative values, while they could be indicative of certain performance metrics, do not inherently correlate with being "below the trend," as they could represent a different context or measure. Therefore,