Producer Price Index Manual: Theory and Practice ... - METAC

7. Treatment of Quality Change
user value and resource cost, both supply and demand
influences. There is, in econometrics terms,
an identification problem, in which the observed
data do not permit the estimation of the underlying
demand and supply parameters. However, suppose
the production technology of sellers is the same
but buyers differ. Then the hedonic function describes
the prices of characteristics the firm will
supply with the given ruling technology to the current
mixture of tastes. There are different tastes on
the consumer side, so what appears in the market is
the result of firms trying to satisfy consumer preferences
all for a constant technology and profit
level; the structure of supply is revealed by the hedonic
price function. Now suppose sellers differ
but buyers’ tastes are the same. Here the hedonic
function p(z) identifies the structure of demand. Of
these possibilities, uniformity of tastes is unlikely
while uniformity of technologies is more likely,
especially when access to technology is unrestricted
in the long run. Griliches (1988, p. 120)
has argued in the context of a CPI:
My own view is that what the hedonic approach
tries to do is to estimate aspects of the budget
constraint facing consumers, allowing thereby
the estimation of “missing” prices when quality
changes. It is not in the business of estimating
utility functions per se, though it can also be useful
for these purposes….what is being estimated
is the actual locus of intersection of the demand
curves of different consumers with varying tastes
and the supply curves of different producers with
possible varying technologies of production. One
is unlikely, therefore to be able to recover the
underlying utility and cost functions from such
data alone, except in very special circumstances.
It is thus necessary to take a pragmatic stance. In
many cases, the implicit quality adjustment to
prices outlined in Section C may be inappropriate
because their implicit assumptions are unlikely to
be valid. The practical needs of economic statistics
require in such instances explicit quality adjustments.
To not do anything on the grounds that the
measures are not conceptually appropriate would
be to ignore the quality change and provide wrong
results. Hedonic techniques provide an important
tool, making effective use of data on the pricequality
relationship derived from other products in
the market to adjustment for changes in one or
more characteristics.
7.137 The proper use of hedonic regression requires
an examination of the coefficients of the estimated
equations to see if they make sense. It
might be argued that the very multitude of distributions
of tastes and technologies and interplay of
supply and demand make it unlikely that reasonable
estimates will arise from such regressions. A
firm may apply and cut a profit margin and prices
for reasons related to long-run strategic plans, for
example, yielding prima facie coefficients that do
not prima facie look reasonable. This does not negate
the usefulness of examining hedonic coefficients
as part of a strategy for evaluating estimated
hedonic equations. First, there has been extensive
empirical work in this field, and the results for individual
coefficients are, for the most part, quite
reasonable. Even over time, individual coefficients
can show quite sensible patterns of decline (van
Mulligen, 2003). Second, as shall be seen, it might
be argued that the prediction and its error should
be our concern and not the values of individual coefficients
(Pakes, 2001).
E.4.3 Implementation
7.138 The implementation of hedonic methods
to estimate quality adjustments to noncomparable
replacements can take a number of forms. The first
form is when the repricing is for a product with
different characteristics. What is required is to adjust
either the price of the old or replacement
(new) product for some valuation of the difference
in quality between the two products. This patching
of missing prices is quite different from the use of
hedonic price indices to be discussed in Section
7.G and Chapter 21. These use hedonic regressions
to provide hedonic price indices of overall qualityadjusted
prices. The former is a partial application,
used on noncomparable replacements when products
are no longer produced. The latter, as will be
seen in Section 7.G, is a general application to a
sample from the whole data set. The partial patching
is considered here.
7.139 Hedonic imputation: predicted vs. actual—In
this approach, a hedonic regression of the
(natural logarithm of the) price of model i in period
t on its characteristics set z t ki
is estimated for
each month, as given by:
t t t t t
(7.24) ln pi = β
0
+ ∑ β
k
zki + εk
.
K
k = 1
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