Work

Estimating the parameters of system dynamics models using indirect inference

Year: 2016

Type: article

Abstract: There is limited methodological guidance for estimating system dynamics (SD) models using datasets common to social sciences that include few data points over time for many units under analysis. Here, we introduce indirect inference, a simulation‐based estimation method that can be applied to common datasets and is applicable to SD models that often include intractable likelihood functions. In this method, the model parameters are found by ensuring that simulated data from the model and available empirical data produce similar auxiliary statistics. The method requires few assumptions about the structure of the model and error‐generating processes and thus can be used in a variety of applications. We demonstrate the method in estimating an SD model of depression and rumination using a panel dataset. The overall results suggest that indirect inference can extend the application of SD models to new topics and leverage common panel datasets to provide unique insights. Copyright © 2016 System Dynamics Society

Source: System Dynamics Review

DOI: 10.1002/sdr.1558

Authors: Niyousha Hosseinichimeh, Hazhir Rahmandad, Mohammad S. Jalali, Andrea K. Wittenborn

Institutions: Virginia Tech, Massachusetts Institute of Technology, Michigan State University

Language: English

Topic: Complex Systems and Decision Making

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

Open Access status: green

OpenAlex ID: W2558572443

FWCI: 4.015

Cites: 30

Cited by: 36

Related to: 10