Aggregation of Heterogeneous Interacting Agents: The Variant Representative Agent Framework

In this paper we have presented a variant of the stochastic aggregation approach which basically consists in exploring the evolution over time of higher moments of the economic units’ distribution. In a sense therefore, we propose to focus on the behavior of a Variant Representative Agent. An application to a classical growth model shows that changes in aggregate output usually associated with Total Factor Productivity in the aggregative interpretation of the framework may be due to changes in the distribution of agents in terms of capital intensity. The application to a model by Gatti et al. (Interaction and market structure, Springer, 2000) shows the efficacy of the method in capturing the evolution over time of the distribution of firms in terms of financial solidity (equity ratio). The method seems general enough to cover a wide range of economic situations in which heterogeneity is relevant and persistent. It seems also simple enough to deserve the attention of the macroeconomist dissatisfied with the RA who wants to derive meaningful and microfounded macroeconomic results.

[1]  D. Gatti,et al.  Agents' Heterogeneity, Aggregation, and Economic Fluctuations , 2000 .

[2]  R. Axtell.  Zipf Distribution of U.S. Firm Sizes , 2001, Science.

[3]  A. Kirman.  Whom Or What Does the Representative Individual Represent , 1992 .

[4]  Masanao Aoki,et al.  New Approaches to Macroeconomic Modeling: Evolutionary Stochastic Dynamics , 1996 .

[5]  Dale W. Jorgenson,et al.  The Transcendental Logarithmic Model of Aggregate Consumer Behavior , 1982 .

[6]  E. Malinvaud.  A framework for aggregation theories , 1993 .

[7]  Masanao Aoki,et al.  Modeling Aggregate Behavior and Fluctuations in Economics: Stochastic Views of Interacting Agents , 2001 .

[8]  B. Greenwald,et al.  Financial Market Imperfections and Business Cycles , 1993 .

[9]  Franklin M. Fisher,et al.  Aggregation : aggregate production functions and related topics , 1992 .

[10]  A. Vercelli,et al.  Macroeconomics : a survey of research strategies , 1992 .

[11]  W. M. Gorman.  Community Preference Fields , 1953 .

[12]  Mauro Gallegati,et al.  Interaction and Market Structure , 2000 .

[13]  H. Kelejian.  Aggregation and Disaggregation of Nonlinear Equations , 1980 .

[14]  Thomas M. Stoker.  Completeness, Distribution Restrictions, and the Form of Aggregate Functions , 1984 .

[15]  J. Stiglitz.  Methodological Issues and the New Keynesian Economics , 1991 .

[16]  H. Minsky,et al.  Information And Coordination , 1981 .

[17]  Christopher A. Sims,et al.  Advances in Econometrics , 1996 .

[18]  A. Lewbel,et al.  Exact Aggregation and a Representative Consumer , 1989 .

[19]  Mauro Gallegati,et al.  On the size distribution of firms: additional evidence from the G7 countries , 2003 .

[20]  Andrew John,et al.  Coordinating Coordination Failures in Keynesian Models , 1988 .

[21]  R. Gibrat,et al.  Les inégalités économiques : applications, aux inégalitês des richesses, a la concentration des entreprises, aux populations des villes, aux statistiques des familles, etc. : d'une loi nouvelle la loi de l'effet proportionnel , 1931 .

[22]  Aggregation of economic relations: A note , 1980 .

[23]  C. Granger.  Long memory relationships and the aggregation of dynamic models , 1980 .

[24]  M. Pesaran.  On Aggregation of Linear Dynamic Models , 1999 .

[25]  James B. Ramsey,et al.  Evaluation of Econometric Models , 1980 .

[26]  H. Theil,et al.  Linear Aggregation of Economic Relations. , 1955 .

[27]  Marco Lippi,et al.  Aggregation and the Microfoundations of Dynamic Macroeconomics , 1998 .

[28]  J. Chavas.  On aggregation and its implications for aggregate behaviour , 1993 .

[29]  Arthur Lewbel,et al.  A Demand System Rank Theorem , 1989 .

[30]  L. Amaral,et al.  Scaling behaviour in the growth of companies , 1996, Nature.