fended on the grounds that coefficients for the variables were robust, that it increased the explanatory power of the models, and that is does not create multicollinearity problems.28 Nonetheless, including brand names is controversial. It is reasonable to worry that the brand variables may “steal” effects from other characteristics—both those that are and that could be included in the model—and thereby bias the estimated effects of characteristics on price. If one assumes that brand, in itself, does not lead to higher valuation by consumers, one must believe that it is an acceptable proxy for unmeasured quality characteristics. Incidence of repairs might be one such example. However, the BLS study on microwave ovens found that the brand names most valued by consumers were actually those with the highest incidence of repair (Liegey, 2000:5). Moreover, brands are repositioned in terms of relative quality from time to time, and reputations sometimes change in response to advertising campaigns, so that brand dummy coefficients may be inherently unstable. Given the difficulty of interpreting coefficients on brand variables, it would be instructive if researchers documented their results with and without brand variables and provided a hypothesis as to what aspects of product value the brand variables are capturing.

The BLS hedonics research program has helped reveal that, in practice, applying hedonic methods to price indexes involves confronting very tough issues. Characteristics cannot be chosen in a formulaic manner—lots of ad hoc judgments are inevitable—and, once chosen, estimated coefficients may exhibit implausible signs.29 Furthermore, models need to be regularly updated because the relationships between characteristics and price are not stable for long periods. For instance, Liegey and Shepler (1999:27) show that new features on VCRs have a large predictive effect on price but, as they become common, their impact quickly recedes. This is a good example of the kind of work BLS must continue to undertake to support expansion of its hedonic program. Investigations into model stability for different product areas are much needed to improve judgments about the frequency with which hedonic regressions should be reestimated.

While the panel believes that the BLS research program is essential to improving understanding of the theoretical uncertainties about hedonic methods, our concerns have not been allayed by what has actually been done so far. Given these ongoing concerns, we are still quite uncomfortable with extending the


Moulton et al. (1998) include indicator variables for brands in their study of televisions. They argue that brand name is important since “a set with the same screen size and other observable characteristics with a premium brand name, such as Sony, may sell for as much as 50 percent more than a similar television from a less prestigious brand” (p. 9). The authors acknowledge that, if additional characteristics could be added to the regression equation, the effect of brand variables might be reduced.


Pakes (2001) has argued that, given rapid entry and exit and great product differentiation in technologically innovative markets, it may not always be clear what the “right” sign on a characteristics variable should be.

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