Evaluating Competing Data Series: A Telecommunications Application

Successful application of theory to practice requires good data. Yet, data can vary widely in quality, consistency, and even longevity of a particular data source. Measurement techniques can change, thus changing the definition of a data series, perhaps because of a business reorganization. In part because of industry change caused by divestiture, there were several alternative sources of data on minutes of telephone demand during the time period covered by this study. A decision had to be made which series, or what combination, to use for forecasting access rates. A canonical correlations model was used to test the similarity of two competing demand series. The number of statistically significant linear combinations indicates the number of demand series that are statistically distinct. If only one linear combination is significant, the canonical correlation algorithm will produce one optimal linear combination of these series to measure demand. The exogenous variables were the ones chosen by the FCC for its aggregate demand model. They included: trend, seasonal dummies, lagged dependent variable, and subscriber lines. The two competing demand series were the dependent variables. Results of the canonical correlation estimation showed two significant linear combinations, showing that the two competing demand series were statistically distinct and indicating that they did not both measure the true demand series. Separate regressions of each demand series on the set of exogenous variables showed that one of them was predominantly explained by the lag of its own dependent variable; the other showed marked seasonality. A comparison with predivestiture data supported the seasonal pattern. As a result of this research, the latter series was considered the more realistic measure of demand. This methodology could be applied similarly where, as was the case with divestiture in telecommunications, organizational discontinuity produces competing data series.