Working Papers 2000 – Abstracts
1/2000
Estimating Demand with Varied Levels of Aggregation
Simone Grose and Keith Mclaren
The response of consumer demand to prices, income, and other characteristics
is important for a range of policy issues. Naturally, the level of detail
for which consumer behaviour can be estimated depends on the level of
disaggregation of the available data. However, it is often the case
that the available data is differently aggregated in different time
periods, with the information available in later time periods usually
being more detailed. The applied researcher is thus faced with choosing
between detail, in which case the more highly aggregated data is ignored;
or duration, in which case the data must be aggregated up to the "lowest
common denominator". Furthermore, since parametric demand systems invariably
involve a large number of parameters, with the number increasing at
least linearly with the number of expenditure categories, it may well
be that only the second option is feasible. That is, there is simply
not enough data available at the finer aggregation level for the chosen
model to be estimated. This paper develops a specification/estimation
technique that exploits the entire information content of a variably-aggregated
data set. The technique is based on the observation that the more highly
aggregated data does in fact contain information on the finer subcategories:
viz, the sum of certain subcategory expenditures is observed. It is
thus possible, under certain simplifying assumptions, to write down,
and maximize, the likelihood of the observed data as a function of the
parameters of the chosen model written for the finest available level
of disaggregation. The technique is applied to an ABS dataset containing
historical information relating to private final consumption expenditures
on up to 18 commodities, and found to be feasible for both the LES and
AIDS.
Keywords: Singular demand systems, Linear
expenditure system, Almost ideal demand system, Missing data.
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