a complement with another (e.g., hardware and software), it is conceptually unclear how to quality adjust each in isolation. Nor does existing theory say much about the importance, or lack thereof, of explanatory power. It is hard to know when a hedonic function is good enough for CPI work: the absence of coefficients with the “wrong” sign may be necessary, but it is surely not sufficient.

When product and process innovations occur, tastes change, or input prices shift, hedonic surfaces may change rapidly. The ability of the BLS or any other agency to capture those changes in real time is, at best, doubtful. It is unclear whether usable estimates of hedonic surfaces can be routinely and rapidly computed for a wide variety of goods. For many goods, the relationship between characteristics and observed price may not be stable, and the best-fitting functional approximation may change across products or time, particularly when technological change is rapid. Research into the stability of coefficients for different product groups is essential for making informed decisions about how often to reestimate hedonic functions. Without this information, reestimation schedules may be dictated by budget or other factors, which might result in outdated adjustments and be worse than doing nothing.

If the hedonic functions were known in every period, some variant of the direct characteristics method would be the best way to derive price ratios. Sometimes this would reduce to the direct time dummy method, but there is no reason to think this would occur frequently. Since the time dummy method has similar data requirements as the direct characteristics method but rests on much stronger assumptions that lack theoretical support—most notably, stable marginal impact of characteristics on price over time—the former has little to recommend it in principle. The time dummy method seems particularly unsuited to index use in rapidly changing product areas for which, presumably, quality adjustment is most warranted.

The biggest obstacle inhibiting use of the direct characteristics approach is that the data and analysis requirements are daunting. However, the payoff from using this approach could be substantial. The methodology can, in principle, produce quality-adjusted indexes that take into account changing marginal relationships between characteristics, weighted by expenditure shares, and price. And, relative to the indirect method that adjusts an observed price change on the basis of individual coefficients, directly produced hedonic indexes are based on the entire hedonic surface which, in theory, generates more robust and precise estimates over different specifications.