tant varieties with at most a one-period lag (see “Technical Note 2” below). Given current technology, estimating hedonic surfaces for, say, September 2000, in time for the release of the corresponding monthly CPI is infeasible for most goods and services.
Due to the narrow range of products for which data requirements can be satisfied, even proponents of the direct characteristics method acknowledge its current limitations. Pakes recommends starting with computers and moving slowly into other areas. Writing about the applicability of hedonics to index construction, he cautions (Pakes, 1997:9):
There are, of course, several detailed decisions which will have to be made before the statistical agencies could produce hedonic adjustments for a set of subindices (among them a decision on the instances in which the hedonic bound is likely to be suspect). Moreover, any shift to hedonics will require prior experimentation by commodity group, and will generate adjustment costs.
These warnings aside, moving in this direction would not require a complete overhaul of BLS pricing methods. In fact, data requirements for the direct characteristics method fit in fairly well with current collection procedures since characteristics must now be tracked in order to judge comparability for replacement situations. Even to improve the CPI under current methodology, better quantity and characteristics data are needed, which is what would also be needed here. In addition, the current requirement that identical products be found at outlets by BLS field agents over index periods might be relaxed since only characteristics— which might be found on a number of similar products—need to be tracked.
Silver (1999:19) suggests that agency data collection needs for hedonic indexes might be met in the future with panels of consumer data for frequently purchased items and scanner data for durables. Paasche, Laspeyres, and superlative formulations could be produced, assuming that the time needed to process comprehensive product scanner data is short enough to allow for base and current period weights to be constructed.
The Boskin commission attributed more than half of its estimated 1.1 percentage point CPI bias to a failure of the index to fully account for changing product quality and the appearance of new goods. The BLS has responded to recommendations by the Boskin commission and others (both before and after Boskin) to address this perceived flaw by expanding its use of hedonic quality adjustment—specifically, the indirect method—in the CPI. Kokoski et al. (2000:3) characterize the hedonic method, or class of methods, as the “currently preferred method of quality adjustment.” The BLS position is that hedonic analysis provides meaningful information for inferring the value consumers place on quality change and that hedonic function estimates based on regression analysis can be reliably used for certain items to make quality adjustments to indexes.