Empirical modelling of the aggregation error in the representative consumer model

Abstract

This paper examines different approaches to modelling the aggregation error associated with the representative consumer model. Each approach is based on an analytical framework intended for modelling aggregate time series data on quantities and prices with potential additional measures of income distribution. Simple functions that track aggregation error over time are found to perform better than more complex and theoretically sophisticated models. An explanation is given based on typical time series characteristics of economic data.

Revisiting Aggregate U.S. Meat Demand with a Bayesian Averaging of Classical Estimates Approach: Do We Need a More General Theory?

“…The next six variables are conditional quantity variables: four aggregate food quantity indexes (cereals x a 10 , dairy x a 11 , fruits and vegetables x a 12 , and other food x a 13 : Moschini, Moro, and Green 1994), and two labor participation variables (women's labor force participation x a 14 and men's labor force participation x a 15 : e.g., Browning and Mehgir 1991;Gao and Shonkwiler 1993). The last variable is a trend variable (x a 16 ) that is designed to capture aggregation error associated with the representative agent specification (Sarmiento and Just 2005), although it is recognized it may proxy for many variables that may change slowly over time. All variables and their sources are described in more detail in the data section later.…”