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A new hybrid metaheuristic method based on biogeography-based optimization and particle swarm optimization algorithm to estimate money demand in Iran
Money demand is one of the most important economic variables which are a critical component in appointing and choosing appropriate monetary policy, because it determines the transmission of policy-driven change in monetary aggregates to the real sector. In this paper, the data of economic indicators...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8374190/ https://www.ncbi.nlm.nih.gov/pubmed/34434749 http://dx.doi.org/10.1016/j.mex.2021.101226 |
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author | Jalaee, Sayyed Abdolmajid Shakibaei, Alireza Horry, Hamid Reza Akbarifard, Hossein GhasemiNejad, Amin Robati, Fateme Nazari Zarin, Naeeme Amani |
author_facet | Jalaee, Sayyed Abdolmajid Shakibaei, Alireza Horry, Hamid Reza Akbarifard, Hossein GhasemiNejad, Amin Robati, Fateme Nazari Zarin, Naeeme Amani |
author_sort | Jalaee, Sayyed Abdolmajid |
collection | PubMed |
description | Money demand is one of the most important economic variables which are a critical component in appointing and choosing appropriate monetary policy, because it determines the transmission of policy-driven change in monetary aggregates to the real sector. In this paper, the data of economic indicators in Iran are presented for estimating the money demand using biogeography-based optimization (BBO) algorithm, particle swarm optimization (PSO) algorithm, and a new hybrid metaheuristic method based on biogeography-based optimization and particle swarm optimization algorithm (BBPSO). The data are used in two forms (i.e. linear and exponential) to estimate money demand values based on true liquidity, Consumer price index, GDP, lending interest rate, Inflation, and official exchange rate. The available data are partly used for finding optimal or near-optimal values of weighting parameters (1974–2013) and partly for testing the models (2014–2018). The performance of methods is evaluated using mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE). According to the simulation results, the proposed method (i.e. BBPSO) outperformed the other models. The findings proved that the recommended method was an appropriate tool for effective money demand prediction in Iran. These data were the result of a comprehensive look at the most influential factors for money market demand. With this method, the demand side of this market was clearly defined. Along with other markets, the consequences of economic policy could be analyzed and predicted. • The article provides a method for observing the effect of economic scenarios on the money market and the analysis obtained by this proposed method allows experts, public sector economics, and monetary economist to see a clearer explanation of the country's liquidity plan. • The method presented in this article can be beneficial for the policy makers and monetary authorities during their decision-making process. |
format | Online Article Text |
id | pubmed-8374190 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-83741902021-08-24 A new hybrid metaheuristic method based on biogeography-based optimization and particle swarm optimization algorithm to estimate money demand in Iran Jalaee, Sayyed Abdolmajid Shakibaei, Alireza Horry, Hamid Reza Akbarifard, Hossein GhasemiNejad, Amin Robati, Fateme Nazari Zarin, Naeeme Amani MethodsX Method Article Money demand is one of the most important economic variables which are a critical component in appointing and choosing appropriate monetary policy, because it determines the transmission of policy-driven change in monetary aggregates to the real sector. In this paper, the data of economic indicators in Iran are presented for estimating the money demand using biogeography-based optimization (BBO) algorithm, particle swarm optimization (PSO) algorithm, and a new hybrid metaheuristic method based on biogeography-based optimization and particle swarm optimization algorithm (BBPSO). The data are used in two forms (i.e. linear and exponential) to estimate money demand values based on true liquidity, Consumer price index, GDP, lending interest rate, Inflation, and official exchange rate. The available data are partly used for finding optimal or near-optimal values of weighting parameters (1974–2013) and partly for testing the models (2014–2018). The performance of methods is evaluated using mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE). According to the simulation results, the proposed method (i.e. BBPSO) outperformed the other models. The findings proved that the recommended method was an appropriate tool for effective money demand prediction in Iran. These data were the result of a comprehensive look at the most influential factors for money market demand. With this method, the demand side of this market was clearly defined. Along with other markets, the consequences of economic policy could be analyzed and predicted. • The article provides a method for observing the effect of economic scenarios on the money market and the analysis obtained by this proposed method allows experts, public sector economics, and monetary economist to see a clearer explanation of the country's liquidity plan. • The method presented in this article can be beneficial for the policy makers and monetary authorities during their decision-making process. Elsevier 2021-01-13 /pmc/articles/PMC8374190/ /pubmed/34434749 http://dx.doi.org/10.1016/j.mex.2021.101226 Text en © 2021 The Author(s) https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Method Article Jalaee, Sayyed Abdolmajid Shakibaei, Alireza Horry, Hamid Reza Akbarifard, Hossein GhasemiNejad, Amin Robati, Fateme Nazari Zarin, Naeeme Amani A new hybrid metaheuristic method based on biogeography-based optimization and particle swarm optimization algorithm to estimate money demand in Iran |
title | A new hybrid metaheuristic method based on biogeography-based optimization and particle swarm optimization algorithm to estimate money demand in Iran |
title_full | A new hybrid metaheuristic method based on biogeography-based optimization and particle swarm optimization algorithm to estimate money demand in Iran |
title_fullStr | A new hybrid metaheuristic method based on biogeography-based optimization and particle swarm optimization algorithm to estimate money demand in Iran |
title_full_unstemmed | A new hybrid metaheuristic method based on biogeography-based optimization and particle swarm optimization algorithm to estimate money demand in Iran |
title_short | A new hybrid metaheuristic method based on biogeography-based optimization and particle swarm optimization algorithm to estimate money demand in Iran |
title_sort | new hybrid metaheuristic method based on biogeography-based optimization and particle swarm optimization algorithm to estimate money demand in iran |
topic | Method Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8374190/ https://www.ncbi.nlm.nih.gov/pubmed/34434749 http://dx.doi.org/10.1016/j.mex.2021.101226 |
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