A study of aggregation bias in estimating the market for home heating and cooling equipment

Econometricians frequently propose parametric models which are contingent on an underlying assumption of rational economic agents maximizing their utility. Accurate estimation of the parameters of these models depends on using data disaggregated to the level of the actual agents, usually individual consumers or firms. Using data at some other level of aggregation introduces bias into the inferences made from the data. Unfortunately, properly disaggregated data is often unavailable, or at least, much more costly to obtain than aggregate data. Research on consumer choice of home heating equipment has long depended on state-level cross-sectional data. Only recently have investigators been able to build up and successfully use data on consumer attributes and choices at the household level. A study estimated for the Electric Power Research Institute REEPS model is currently one of the best of these. This paper examines the degree of bias that would be introduced in that study if only average data across SMSAs or states were used at several points in the investigation. We examine the market shares and elasticities estimated from that model using only the mean values of the exogenous variables, and find severe errors to be possible. However, if the models were calibrated on only aggregate data originally, we find that proper treatment allows market shares and elasticities to be found with little error relative to the disaggregate models. 22 refs., 4 figs., 10 tabs.