Parameter estimation through the weighted goal programming model

Belaid Aouni, Cinzia Colapinto, Davide La Torre

Research output: Contribution to journalArticle

Abstract

Many models in economics, management and finance can be described in terms of nonlinear dynamical systems which usually depend on some unknown parameters. To conduct a long-run behaviour analysis of these models it is of paramount importance to establish efficient and accurate parameter estimation techniques. Today many sophisticated nonlinear model estimation, selection and testing approaches are available and reliable. However, when the nonlinear dynamical systems take the form of differential equations, many of them fail and it is required to use more advanced techniques. The aim of this paper is to present a weighted goal programming formulation for estimating the unknown parameters of dynamical models described in terms of differential equations. The method is illustrated through two different applications to population dynamics (Malthus model) and innovation diffusion (Bass model).

Original languageEnglish
Pages (from-to)263-273
Number of pages11
JournalInternational Journal of Multicriteria Decision Making
Volume5
Issue number3
DOIs
Publication statusPublished - Jan 1 2015

Fingerprint

Goal programming
Parameter estimation
Differential equations
Nonlinear dynamical systems
Bass model
Economics
Innovation diffusion
Finance
Malthus
Behavior analysis
Population dynamics
Testing

Keywords

  • Bass model
  • Malthus model
  • Parameter estimation
  • Weighted goal programming model

ASJC Scopus subject areas

  • Strategy and Management
  • Management Science and Operations Research

Cite this

Parameter estimation through the weighted goal programming model. / Aouni, Belaid; Colapinto, Cinzia; Torre, Davide La.

In: International Journal of Multicriteria Decision Making, Vol. 5, No. 3, 01.01.2015, p. 263-273.

Research output: Contribution to journalArticle

@article{fc73ca6e93df440dad1833859b041c72,
title = "Parameter estimation through the weighted goal programming model",
abstract = "Many models in economics, management and finance can be described in terms of nonlinear dynamical systems which usually depend on some unknown parameters. To conduct a long-run behaviour analysis of these models it is of paramount importance to establish efficient and accurate parameter estimation techniques. Today many sophisticated nonlinear model estimation, selection and testing approaches are available and reliable. However, when the nonlinear dynamical systems take the form of differential equations, many of them fail and it is required to use more advanced techniques. The aim of this paper is to present a weighted goal programming formulation for estimating the unknown parameters of dynamical models described in terms of differential equations. The method is illustrated through two different applications to population dynamics (Malthus model) and innovation diffusion (Bass model).",
keywords = "Bass model, Malthus model, Parameter estimation, Weighted goal programming model",
author = "Belaid Aouni and Cinzia Colapinto and Torre, {Davide La}",
year = "2015",
month = "1",
day = "1",
doi = "10.1504/IJMCDM.2015.071251",
language = "English",
volume = "5",
pages = "263--273",
journal = "International Journal of Multicriteria Decision Making",
issn = "2040-106X",
publisher = "Inderscience Publishers",
number = "3",

}

TY - JOUR

T1 - Parameter estimation through the weighted goal programming model

AU - Aouni, Belaid

AU - Colapinto, Cinzia

AU - Torre, Davide La

PY - 2015/1/1

Y1 - 2015/1/1

N2 - Many models in economics, management and finance can be described in terms of nonlinear dynamical systems which usually depend on some unknown parameters. To conduct a long-run behaviour analysis of these models it is of paramount importance to establish efficient and accurate parameter estimation techniques. Today many sophisticated nonlinear model estimation, selection and testing approaches are available and reliable. However, when the nonlinear dynamical systems take the form of differential equations, many of them fail and it is required to use more advanced techniques. The aim of this paper is to present a weighted goal programming formulation for estimating the unknown parameters of dynamical models described in terms of differential equations. The method is illustrated through two different applications to population dynamics (Malthus model) and innovation diffusion (Bass model).

AB - Many models in economics, management and finance can be described in terms of nonlinear dynamical systems which usually depend on some unknown parameters. To conduct a long-run behaviour analysis of these models it is of paramount importance to establish efficient and accurate parameter estimation techniques. Today many sophisticated nonlinear model estimation, selection and testing approaches are available and reliable. However, when the nonlinear dynamical systems take the form of differential equations, many of them fail and it is required to use more advanced techniques. The aim of this paper is to present a weighted goal programming formulation for estimating the unknown parameters of dynamical models described in terms of differential equations. The method is illustrated through two different applications to population dynamics (Malthus model) and innovation diffusion (Bass model).

KW - Bass model

KW - Malthus model

KW - Parameter estimation

KW - Weighted goal programming model

UR - http://www.scopus.com/inward/record.url?scp=84940529269&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84940529269&partnerID=8YFLogxK

U2 - 10.1504/IJMCDM.2015.071251

DO - 10.1504/IJMCDM.2015.071251

M3 - Article

AN - SCOPUS:84940529269

VL - 5

SP - 263

EP - 273

JO - International Journal of Multicriteria Decision Making

JF - International Journal of Multicriteria Decision Making

SN - 2040-106X

IS - 3

ER -