A Neural Network Scheme for Monetary Policy Rate Validation in Nigeria

Oloruntoba Samuel Ogundele; Augustine Ujunwa; Aminu Ado Mohammed

doi:doi:10.11648/j.ajai.20200402.13

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A Neural Network Scheme for Monetary Policy Rate Validation in Nigeria

Oloruntoba Samuel Ogundele, Augustine Ujunwa, Aminu Ado Mohammed

Published in American Journal of Artificial Intelligence (Volume 4, Issue 2)

Received: 4 October 2020 Accepted: 12 November 2020 Published: 11 December 2020

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Abstract

This research work is an exploratory study that tried to examine the viability of adopting artificial neural network (ANN), an aspect of machine learning in the analysis of monetary data for the design and validation of monetary policy from both optimistic and normative approach. Methodologically, the research is motivated by the work of [33] which used the Greenbook real time data of the U.S. Federal Reserve's in the analysis of monetary policy reaction functions in forecasting performance using ANN. Following the work on the adoption of this technique, we tried to develop a framework based on machine learning for policy rate forecasting by analysing macroeconomic data with the aim of guiding and aiding monetary authority in making monetary policy decisions. From the results, the ANN perform better in predicting the monetary policy rate compared to the linear models and the univariate process. It also revealed the non-linearity in the behavior of the monetary policy rate in Nigeria during the study period. While the work does not mean to advocate that machine will replace human-being in policy rate determination in the monetary policy-making process; we believe that the development and implementation of this system would support building effective prediction system which can be validated. The result from the designed system is expected to enhance credibility, confidence and transparency of central banks in making an independent decision (s) based on objective forecasts and implied analysis in setting policy through a well-structured comparison of results.

Published in	American Journal of Artificial Intelligence (Volume 4, Issue 2)
DOI	10.11648/j.ajai.20200402.13
Page(s)	62-72
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2020. Published by Science Publishing Group

Keywords

Monetary Policy, Artificial Neural Network, Taylor Rule, Data

References

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APA Style

Oloruntoba Samuel Ogundele, Augustine Ujunwa, Aminu Ado Mohammed. (2020). A Neural Network Scheme for Monetary Policy Rate Validation in Nigeria. American Journal of Artificial Intelligence, 4(2), 62-72. https://doi.org/10.11648/j.ajai.20200402.13

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ACS Style

Oloruntoba Samuel Ogundele; Augustine Ujunwa; Aminu Ado Mohammed. A Neural Network Scheme for Monetary Policy Rate Validation in Nigeria. Am. J. Artif. Intell. 2020, 4(2), 62-72. doi: 10.11648/j.ajai.20200402.13

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AMA Style

Oloruntoba Samuel Ogundele, Augustine Ujunwa, Aminu Ado Mohammed. A Neural Network Scheme for Monetary Policy Rate Validation in Nigeria. Am J Artif Intell. 2020;4(2):62-72. doi: 10.11648/j.ajai.20200402.13

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@article{10.11648/j.ajai.20200402.13,
  author = {Oloruntoba Samuel Ogundele and Augustine Ujunwa and Aminu Ado Mohammed},
  title = {A Neural Network Scheme for Monetary Policy Rate Validation in Nigeria},
  journal = {American Journal of Artificial Intelligence},
  volume = {4},
  number = {2},
  pages = {62-72},
  doi = {10.11648/j.ajai.20200402.13},
  url = {https://doi.org/10.11648/j.ajai.20200402.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajai.20200402.13},
  abstract = {This research work is an exploratory study that tried to examine the viability of adopting artificial neural network (ANN), an aspect of machine learning in the analysis of monetary data for the design and validation of monetary policy from both optimistic and normative approach. Methodologically, the research is motivated by the work of [33] which used the Greenbook real time data of the U.S. Federal Reserve's in the analysis of monetary policy reaction functions in forecasting performance using ANN. Following the work on the adoption of this technique, we tried to develop a framework based on machine learning for policy rate forecasting by analysing macroeconomic data with the aim of guiding and aiding monetary authority in making monetary policy decisions. From the results, the ANN perform better in predicting the monetary policy rate compared to the linear models and the univariate process. It also revealed the non-linearity in the behavior of the monetary policy rate in Nigeria during the study period. While the work does not mean to advocate that machine will replace human-being in policy rate determination in the monetary policy-making process; we believe that the development and implementation of this system would support building effective prediction system which can be validated. The result from the designed system is expected to enhance credibility, confidence and transparency of central banks in making an independent decision (s) based on objective forecasts and implied analysis in setting policy through a well-structured comparison of results.},
 year = {2020}
}

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TY  - JOUR
T1  - A Neural Network Scheme for Monetary Policy Rate Validation in Nigeria
AU  - Oloruntoba Samuel Ogundele
AU  - Augustine Ujunwa
AU  - Aminu Ado Mohammed
Y1  - 2020/12/11
PY  - 2020
N1  - https://doi.org/10.11648/j.ajai.20200402.13
DO  - 10.11648/j.ajai.20200402.13
T2  - American Journal of Artificial Intelligence
JF  - American Journal of Artificial Intelligence
JO  - American Journal of Artificial Intelligence
SP  - 62
EP  - 72
PB  - Science Publishing Group
SN  - 2639-9733
UR  - https://doi.org/10.11648/j.ajai.20200402.13
AB  - This research work is an exploratory study that tried to examine the viability of adopting artificial neural network (ANN), an aspect of machine learning in the analysis of monetary data for the design and validation of monetary policy from both optimistic and normative approach. Methodologically, the research is motivated by the work of [33] which used the Greenbook real time data of the U.S. Federal Reserve's in the analysis of monetary policy reaction functions in forecasting performance using ANN. Following the work on the adoption of this technique, we tried to develop a framework based on machine learning for policy rate forecasting by analysing macroeconomic data with the aim of guiding and aiding monetary authority in making monetary policy decisions. From the results, the ANN perform better in predicting the monetary policy rate compared to the linear models and the univariate process. It also revealed the non-linearity in the behavior of the monetary policy rate in Nigeria during the study period. While the work does not mean to advocate that machine will replace human-being in policy rate determination in the monetary policy-making process; we believe that the development and implementation of this system would support building effective prediction system which can be validated. The result from the designed system is expected to enhance credibility, confidence and transparency of central banks in making an independent decision (s) based on objective forecasts and implied analysis in setting policy through a well-structured comparison of results.
VL  - 4
IS  - 2
ER  -

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Author Information

Oloruntoba Samuel Ogundele

Information Technology Unit, West African Monetary Institute, Accra, Ghana
Augustine Ujunwa

Monetary Policy Department, Central Bank of Nigeria, Abuja, Nigeria
Aminu Ado Mohammed

Monetary Policy Department, Central Bank of Nigeria, Abuja, Nigeria

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APA Style

Oloruntoba Samuel Ogundele, Augustine Ujunwa, Aminu Ado Mohammed. (2020). A Neural Network Scheme for Monetary Policy Rate Validation in Nigeria. American Journal of Artificial Intelligence, 4(2), 62-72. https://doi.org/10.11648/j.ajai.20200402.13

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ACS Style

Oloruntoba Samuel Ogundele; Augustine Ujunwa; Aminu Ado Mohammed. A Neural Network Scheme for Monetary Policy Rate Validation in Nigeria. Am. J. Artif. Intell. 2020, 4(2), 62-72. doi: 10.11648/j.ajai.20200402.13

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AMA Style

Oloruntoba Samuel Ogundele, Augustine Ujunwa, Aminu Ado Mohammed. A Neural Network Scheme for Monetary Policy Rate Validation in Nigeria. Am J Artif Intell. 2020;4(2):62-72. doi: 10.11648/j.ajai.20200402.13

Copy | Download

@article{10.11648/j.ajai.20200402.13,
  author = {Oloruntoba Samuel Ogundele and Augustine Ujunwa and Aminu Ado Mohammed},
  title = {A Neural Network Scheme for Monetary Policy Rate Validation in Nigeria},
  journal = {American Journal of Artificial Intelligence},
  volume = {4},
  number = {2},
  pages = {62-72},
  doi = {10.11648/j.ajai.20200402.13},
  url = {https://doi.org/10.11648/j.ajai.20200402.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajai.20200402.13},
  abstract = {This research work is an exploratory study that tried to examine the viability of adopting artificial neural network (ANN), an aspect of machine learning in the analysis of monetary data for the design and validation of monetary policy from both optimistic and normative approach. Methodologically, the research is motivated by the work of [33] which used the Greenbook real time data of the U.S. Federal Reserve's in the analysis of monetary policy reaction functions in forecasting performance using ANN. Following the work on the adoption of this technique, we tried to develop a framework based on machine learning for policy rate forecasting by analysing macroeconomic data with the aim of guiding and aiding monetary authority in making monetary policy decisions. From the results, the ANN perform better in predicting the monetary policy rate compared to the linear models and the univariate process. It also revealed the non-linearity in the behavior of the monetary policy rate in Nigeria during the study period. While the work does not mean to advocate that machine will replace human-being in policy rate determination in the monetary policy-making process; we believe that the development and implementation of this system would support building effective prediction system which can be validated. The result from the designed system is expected to enhance credibility, confidence and transparency of central banks in making an independent decision (s) based on objective forecasts and implied analysis in setting policy through a well-structured comparison of results.},
 year = {2020}
}

Copy | Download

TY  - JOUR
T1  - A Neural Network Scheme for Monetary Policy Rate Validation in Nigeria
AU  - Oloruntoba Samuel Ogundele
AU  - Augustine Ujunwa
AU  - Aminu Ado Mohammed
Y1  - 2020/12/11
PY  - 2020
N1  - https://doi.org/10.11648/j.ajai.20200402.13
DO  - 10.11648/j.ajai.20200402.13
T2  - American Journal of Artificial Intelligence
JF  - American Journal of Artificial Intelligence
JO  - American Journal of Artificial Intelligence
SP  - 62
EP  - 72
PB  - Science Publishing Group
SN  - 2639-9733
UR  - https://doi.org/10.11648/j.ajai.20200402.13
AB  - This research work is an exploratory study that tried to examine the viability of adopting artificial neural network (ANN), an aspect of machine learning in the analysis of monetary data for the design and validation of monetary policy from both optimistic and normative approach. Methodologically, the research is motivated by the work of [33] which used the Greenbook real time data of the U.S. Federal Reserve's in the analysis of monetary policy reaction functions in forecasting performance using ANN. Following the work on the adoption of this technique, we tried to develop a framework based on machine learning for policy rate forecasting by analysing macroeconomic data with the aim of guiding and aiding monetary authority in making monetary policy decisions. From the results, the ANN perform better in predicting the monetary policy rate compared to the linear models and the univariate process. It also revealed the non-linearity in the behavior of the monetary policy rate in Nigeria during the study period. While the work does not mean to advocate that machine will replace human-being in policy rate determination in the monetary policy-making process; we believe that the development and implementation of this system would support building effective prediction system which can be validated. The result from the designed system is expected to enhance credibility, confidence and transparency of central banks in making an independent decision (s) based on objective forecasts and implied analysis in setting policy through a well-structured comparison of results.
VL  - 4
IS  - 2
ER  -

Copy | Download