Arbeitspapier

Computing longitudinal moments for heterogeneous agent models

Computing population moments for heterogeneous agent models is a necessary step for their estimation and evaluation. Computation based on Monte Carlo methods is usually time- and resource-consuming because it involves simulating a large sample of agents and potentially tracking them over time. We argue in favor of an alternative method for computing both cross-sectional and longitudinal moments that exploits the endogenous Markov transition function that defines the stationary distribution of agents in the model. The method relies on following the distribution of populations of interest by iterating forward the Markov transition function rather than focusing on a simulated sample of agents. Approximations of this function are readily available from standard solution methods of dynamic programming problems. The method provides precise estimates of moments like top-wealth shares, auto-correlations, transition rates, or age-profiles, at lower time- and resource-costs compared to Monte Carlo based methods.

Sprache
Englisch

Erschienen in
Series: Research Report ; No. 2022-10

Klassifikation
Wirtschaft
Thema
Computational Methods
Heterogeneous Agents
Simulation

Ereignis
Geistige Schöpfung
(wer)
Ocampo Díaz, Sergio
Robinson, Baxter
Ereignis
Veröffentlichung
(wer)
The University of Western Ontario, Department of Economics
(wo)
London (Ontario)
(wann)
2022

Handle
Letzte Aktualisierung
10.03.2025, 11:43 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Arbeitspapier

Beteiligte

  • Ocampo Díaz, Sergio
  • Robinson, Baxter
  • The University of Western Ontario, Department of Economics

Entstanden

  • 2022

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