It is essential to note that hedonic techniques expose a purely empirical relationship between prices and variation among different models of a good. The results of hedonic regressions can be used in either a COGI or a COLI framework. Zvi Griliches, who helped pioneer the application of hedonic methods to price index construction, commented in 1976—and cited the comment approvingly almost 15 year later (Griliches, 1990:189):

What the hedonic approach attempted was to provide a tool for estimating “missing” prices, prices of particular bundles not observed in the original or later periods. It did not pretend to dispose of the question of whether the various observed differentials are demand or supply oriented, how the observed variety of models in the market is generated, and whether the resulting indexes have an unambiguous welfare interpretation. . . . Its goals were modest. It offered the tool of econometrics, with all its attendant problems, as a help to the solution of the first two issues, the detection of the relevant characteristics of a commodity and the estimation of their marginal market valuation.

One potential advantage of hedonics is that a market may offer products that display a constant set of characteristics over time, even though specific models (and the corresponding characteristic bundles) change. Moreover, in some cases the link between what consumers ultimately value and product characteristics may be more intuitive than the link to a product itself. To this point, Griliches (1990:191) wrote: “Buried within the hedonic idea was the germ of Becker’s (1965) ‘household production function’ and the notion that one should look at the relevant activity as a whole, at its ‘ultimate’ product in terms of utility or productivity, and not just at the individual components.”

A hedonic function relates the price, pit, of variety or model i of some product in some period t, to a vector of its relevant characteristics, zit : pit = ht(zit).16 In the examples of butter and gasoline discussed at the start of this chapter, z consists of a single variable (ounces of butter and miles of driving, respectively), and there was an implicit presumption that h should be simply proportional to that variable. In more realistic cases, there are multiple relevant characteristics, and h is generally not a linear function of their values. In a typical hedonic regression, price, or the logarithm of price, is the dependent variable, and identifiable and quantifiable product characteristics serve as the explanatory variables.17 In a well-specified equation, coefficients on the explanatory variables reveal the marginal relationship between the product characteristics and price at

16  

Econometric estimation of hedonic functions dates back at least to the work of Waugh (1928) and Court (1939). This approach received considerable impetus from the seminal work of Griliches (1961).

17  

Interaction terms and nonlinear transformations are also sometimes employed. Some models call for additional explanatory variables such as time period indicators, outlet type, or brand name that may not always be directly indicative of product quality. The implications of the latter additions are discussed below.



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