I am creating and using OLS Regression models using historical data to forecast quarterly balances for banking products (loans, mortgages, deposits, etc) for the Dodd-Frank/CCAR exercises. One problem we have run into is that sometimes the last historical time period's value (jump-off point which is used to start forecasting from) can be unexpectedly high or low (possibly due to a business action like a temporary interest rate change for marketing purposes or maybe an unusual event in the marketplace). This creates a problem in that the forecast generated is unusually high or low due to the out of the ordinary jump-off point. Any ideas on how to adjust for this in the model? An initial thought is to forecast from a previous data point where the growth rate of the data point is within a certain acceptable range. Any thoughts, ideas, or references to scholarly papers on this topic would be helpful. Thanks.
Thanks for the reply PG. Actually Dodd-Frank/CCAR can be put aside. Basically I have a historical time series of quarterly data that I used to develop an OLS regression equation, and I am using the OLS regression equation to forecast future time points. The problem is that the last historical time point IS an outlier. So my question is if I still want to use that last (outlier) point as my jump off point to start forecasting, is there a way to adjust either that point or the future forecast to take that issue into account? Currently my forecast is unreasonable since the jump off (last historical) point was unreasonable to start with.
You could use, say, the 4-quarter trailing average for the jump-off date.
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