constant over time are in fact not constant, the estimated time dummies will reflect a mixture of pure price changes and quality changes, and the resulting index will be biased. More generally, there is neither theoretical support nor much empirical evidence for the assumption that prices of all varieties of particular products generally move proportionately over time.

The second direct approach, variants of which have been suggested by Pakes and Levinsohn (1993), Feenstra (1995), Diewert (2001), and others, is what we call the direct characteristics method. Under this approach one estimates a separate hedonic function for each period and computes price relatives for the product under study by, in effect, comparing the functions for the periods involved. The idea is not to require that all estimated nondummy coefficients differ between periods; it is rather to impose between-period coefficient equality only when that hypothesis withstands statistical scrutiny. To the extent that data from multiple periods can be pooled, estimation efficiency (always a concern in these studies) can be enhanced.23 In contrast to the time dummy approach, the direct characteristics index is—as its name denotes—constructed from the characteristics coefficients, which are in general allowed to vary over time. The method also offers an advantage over the BLS’s deletion or indirect hedonics methods in that it allows for correction of any sample selection bias that may be created because price changes are only sampled from the set of goods or services that remain unchanged from period to period.

However, despite its conceptual appeal, there are reasons that, given the current state of the art, the direct characteristics approach does not have broad applicability across CPI categories. One issue, which applies to all direct methods, involves the general problem of price data that reflect nonobservable seller attributes. Outlet bias (discussed in detail in Chapter 5), for instance, is difficult to control for in an index produced from a time dummy regression or by relating hedonic functions for successive periods. In contrast to other methods in which prices for replacement items are quoted from the same outlet, product price and characteristics data are combined from multiple sources to estimate direct hedonic indexes (Triplett, 2001b:3).

The most obvious obstacle to widespread use of direct hedonic methods, though, involves the data requirements and the operational difficulty of producing characteristics-based indexes on a high-frequency, up-to-date schedule. To produce such an index, routine data collection and processing procedures would need to be directed toward monthly pricing of a comprehensive set of varieties, chosen to represent a population’s consumption, rather than a limited sample. Most importantly, it would be necessary to gather data on the sales of all impor-

23  

Two related direct approaches, both of which give the same result as the direct time dummy method when its assumption of stability of nondummy coefficients (and thus of proportional shifts in prices of all varieties) is satisfied are discussed below in “Technical Note 2.”



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