In most data applications, staticians must identify and estimate outlier effects. When doing seasonal adjustment, we are concerned that outliers may interfere with estimation of seasonal effects. By removing outlier effects, we hope to produce the best possible seasonal adjustment. The autocorrelation structure of time series differs from that of other types of data, so the outlier selection techniques also must be different. Using a large sample of economic time series from the U.S. Census Bureau, we fit regARIMA models (regression models with ARIMA errors) to the data with the X-12-ARIMA seasonal adjustment program. Our research simulated production as we added data one month at a time, refitting the regARIMA models for each run. We looked at the performance of automatic outlier identification when we raised or lowered the critical value, and we compared that to visual outlier selection methods. We expected our visual selection methods to improve on automatic outlier identification, but we concluded that the current automatic identification procedure was generall the best method.