Academic journal article American Economist

Evaluating Competing Data Series: A Telecommunications Application

Academic journal article American Economist

Evaluating Competing Data Series: A Telecommunications Application

Article excerpt

Victor Glass [*]

Susanna Cahn [**]


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.


With the breakup of the Bell system in 1984, a single long distance telephone call became a transaction that might involve several companies: the caller's local telephone company ("local exchange carrier" or LEC), the receiver's LEC and a long distance telephone company ("Interexchange carrier" or IC). The caller pays the long distance company for these calls, but the long distance company is charged an "access rate" for using the local telephone network. Access rates were introduced by the FCC at the time of the divestiture of AT&T. In 1983, the FCC also mandated the development of the National Exchange Carrier Association (NECA) to administer access rates. An organization unknown outside the telephone industry, NECA has replaced AT&T as the focal point for setting rates and clearing revenues to local exchange carriers who are members of NECA.

NECA prepares and justifies tariffs and distributes revenues on behalf of its member companies in much the same way that AT&T did prior to divestiture. Access rates are set on a unit cost basis with costs including an FCC authorized rate of return on net investment. Rates are set on a prospective basis: costs and volumes are forecasted to set rates. Net investment is multiplied by the authorized rate of return set by the FCC and added to operating expenses to derive revenue requirements. For those costs that are sensitive to the volume of telephone calls, access rates are derived by dividing revenue requirements by volumes (e.g. minutes). One of these rates, the carrier common line rate, is set on a nationwide basis. All other access rates are set on a state-specific basis.

NECA is responsible for achieving the authorized rate of return for its members, The authorized rate of return is set by the FCC and is considered a target rate by the Commission. …

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