1 Introduction
Most business cycle research focuses on the behavior of the representative agent model
and neglects the effects that are caused by the heterogeneity of agents. Notable ex-
ceptions are Rfos-Rull (1996) and Castaneda, Diaz-Giminenez, and Rfos-Rull (1998),
among others. Rfos-Rull (1996) considers a stochastic OLG model for the study of the
effects of a technology shock. He shows that the business cycle dynamics of the life-
cycle model is basically the same as those of the infinitely-lived representative-agent
model. Castanneda et al. (1998) explore the dynamics of the income distribution in a
neoclassical growth model with heterogeneous infinitely-lived households. They demon-
strate that five types of households are enough to replicate most of the findings on the
US income distribution business cycle dynamics. Both studies consider a Walrasian
economy. The present paper is most closely related to the one of Rfos-Rull (1996).
Different from his model, agents are also heterogeneous within generations. For this
reason, we are also able to replicate the observed income and wealth heterogeneity. In
addition to both Rfos-Rull (1996) and Castaneda et al. (1998), we introduce money
and sticky prices in our model so that we are able to also consider a monetary shock in
addition to the productivity shock. Furthermore, we calibrate the model period equal
to one quarter rather than one year.1 We consider a model period of one quarter to be
more appropriate for the study of business cycle dynamics.
In essence, we confirm the results of Rfos-Rull (1996). The heterogeneous-agent OLG
model behaves almost identical to the representative-agent model. We, however, find
one noteworthy exception. Following a technology shock, aggregate hours decrease in
the OLG model whereas they increase in the Ramsey model. In order to observe this
effect in our model we need both the productivity-age profile and the heterogeneity
within generations. A productivity shock then implies a heterogeneous response of
the labor supply among workers depending on their productivity, their age, and their
wealth. In this respect our paper provides another explanation for the negative response
of working hours to a technology shock that Galf (1999) and Francis and Ramey (2002)
identify in vector autoregressions. Francis and Ramey (2002) show that this finding
1 In addition, we also apply a solution method that has not been used to the computation of such
large-scale stochastic models before. While Rlos-Rull (1996) uses a solution method that is only
applicable to a central-planning problem, Castaneda et al. (1998) use the algorithm suggested by
Krussell and Smith (1998). Our study, therefore, is also of independent interest for the researcher of
computational methods.