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

| Peer-Reviewed

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

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

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), 2024. Published by Science Publishing Group

Keywords

Monetary Policy, Artificial Neural Network, Taylor Rule, Data

References

[1]	Ben S. Bernanke, Jean Boivin, “Monetary policy in a data-rich environment”, Journal of Monetary Economics 50 (2003) 525–546, doi: 10.1016/S0304-3932(03)00024-2.
[2]	Attention, shoppers: Store is tracking your cell, New York Times. URL: http://www.nytimes.com/2013/07/15/business/attention-shopper-storesare-tracking-your-cell.html.
[3]	Friedman M Benjamin, A Program for Monetary Stability. 1959. New York: Macmillan.
[4]	Friedman M Benjamin, Monetary Policy, Fiscal Policy, and the Efficiency of Our Financial System: Lessons from the Financial Crisis, International Journal of Central Banking, Vol. 8 No. S1, 2012, pp 301-309, https://www.ijcb.org/journal/ijcb12q0a20.pdf.
[5]	M. Naimur Rahman, et al, Machine Learning with Big Data An Efficient Electricity Generation Forecasting System, Big Data Research, (2016), http://dx.doi.org/10.1016/j.bdr.2016.02.002.
[6]	P. S. Yu, On mining big data, in: J. Wang, H. Xiong, Y. Ishikawa, J. Xu, J. Zhou (Eds.), Web-Age Information Management, in: Lecture Notes in Computer Science, vol. 7923, Springer-Verlag, Berlin, Heidelberg, 2013, p. XIV.
[7]	Taylor, J. B. 1979. “Estimation and control of a macroeconomic model with rational expectations.” Econometrica, 47 (5): 1267–86.
[8]	Taylor, J. B. 1993. “Discretion versus policy rules in practice”. Carnegie Rochester Series on Public Policy, 39: 195–214, North Holland.
[9]	Taylor, J. B. 1999. “A historical analysis of monetary policy rules”. In J. Taylor, ed., Monetary Policy Rules, Business Cycle Series, 31: 319–348. University of Chicago Press, Chicago.
[10]	Stock, J. H., Watson, M. W., 1989. New indexes of coincident and leading economic indicators. NBER Macroeconomics Annual 4. MIT Press, Cambridge, MA.
[11]	Stock, J. H., Watson, M. W., 1999. Forecasting Inflation. Journal of Monetary Economics 44 (2), 293–335.
[12]	Stock, J. H., Watson, M. W., 2002. Macroeconomic forecasting using diffusion indexes. Journal of Business & Economic Statistics 20 (2), 147–162.
[13]	P. F. Pai, W. C. Hong, Forecasting regional electricity load based on recurrent sup-port vector machines with genetic algorithms, Electr. Power Syst. Res. 74 (3) (2005) 417–425.
[14]	P. C. Chang, Y. W. Wang, C. H. Liu, Fuzzy Delhi and Backpropagation model for sales forecasting in PCB industry, Expert Syst. Appl. 30 (4) (2006) 715–726.
[15]	Wikipedia contributor (2014, Jan 10), Backpropagation (version ID: 634418284) [online], available: http://en.wikipedia.org/wiki/Backpropagation.
[16]	Wikipedia contributor (2014, April 20), Artificial Neural Network (version ID: 174595685) [online], available: http://en.wikipedia.org/wiki/Artificial_neural_network.
[17]	Christiano, Lawrence J. & Eichenbaum, Martin & Evans, Charles L., 1999. "Monetary policy shocks: What have we learned and to what end?," Handbook of Macroeconomics, in: J. B. Taylor & M. Woodford (ed.), Handbook of Macroeconomics, edition 1, volume 1, chapter 2, pages 65-148, Elsevier.
[18]	Wikipedia contributor (2013, Nov 23), Machine Learning (version ID: 175534373) [online], available: http://en.wikipedia.org/wiki/Machine_learning, 2013, Web-12 Jan, 2014.
[19]	Wikipedia contributor (2016, Jan 16) [online], Cognitive Science (version ID: 699869928) [online], available: https://en.wikipedia.org/wiki/Cognitive_science.
[20]	Wikipedia contributor (2015, May 10), List of Machine Learning con-cepts (version ID: 685871451)[online], available: http://en.wikipedia.org/wiki/List_of_machine_learning_concepts.
[21]	Wikipedia contributor (2015, April 12), Data Science (version ID: 7000450339) [online], available: http://en.wikipedia.org/wiki/Data_science.
[22]	Hornik, K, Stinchcombe, M, and White, H. Multilayer feedforward networks are universal approximators. Neural Networks, 2 (5): 359-366, 1989.
[23]	Orphanides Athanasios, “Monetary Policy Rules Based on Real-Time Data”, American Economic Review vol. 91, no. 4, September 2001 (pp. 964-985).
[24]	Orphanides, Athanasios, Taylor Rules: FEDS Working Paper No. 2007-18. Available at SSRN: https://ssrn.com/abstract=999563 or http://dx.doi.org/10.2139/ssrn.999563.
[25]	Malliaris A. G and M. Malliaris. Modeling federal funds rates: A comparison of four methodologies. Neural Computing and Applications, 18 (1): 37-44, 2009.
[26]	Svensson, L. Inflation forecast targeting: Implementing and monitoring inflation targets. European Economic Review, 41 (6): 1111-1146, 1997.
[27]	Teräsvirta T, Van Dijk, D and M. C. Medeiros, M. C.. Linear models, smooth transition autoregressions, and neural networks for forecasting macroeconomic time series: A re-examination. International Journal of Forecasting, 21 (4): 755774, 2005.
[28]	Gonzalez, S. Neural networks for macroeconomic forecasting: A complementary approach to linear regression models. Working Paper, Department of Finance Canada, 2000.
[29]	Moura Marcelo and Carvalho de Alexandre, What can Taylor rules say about monetary policy in Latin America? Journal of Macroeconomics, 2010, vol. 32, issue 1, 392-404.
[30]	Hammond, Gill, Kanbur, Ravi, and Prasad, Eswar S. Monetary Policy Challenges for Emerging Market Economies (August 4, 2009). Brookings Global Economy and Development Working Paper No. 36. Available at SSRN: https://ssrn.com/abstract=1492191 or http://dx.doi.org/10.2139/ssrn.1492191.
[31]	Mohanty, M. S. & Klau Marc, Monetary policy rules in emerging market economies: issues and evidence, BIS Working Papers No. 149, 2004. Bank for International Settlements.
[32]	Egeli Birgul, Ozturan Meltem, Badur Bertan, Stock Market Prediction Using Artificial Neural Networks http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.134.1114.
[33]	Hinterlang, Natascha, Predicting Monetary Policy Using Artificial Neural Networks, Contributions to the Annual Meeting of the Social Policy Association 2019: 30 years Fall of the Wall - Democracy and Market Economy - Session: Econometrics - Forecasting I, No. B07-V3, ZBW - Leibniz Information Center for Economic Affairs, Kiel, Hamburg, https://www.econstor.eu/bitstream/10419/203503/1/VfS-2019-pid-25781.pdf.
[34]	B. Franks, Taming the Big Data Tidal Wave: Finding Opportunities in Huge Data Streams with Advanced Analytics, first ed., in: Wiley and SAS Business Series, Wiley, 2012.
[35]	Haykin, S., Neural Neworks: A Comprehensive Foundation. 2nd Edition, 1999. Prentice- Hall, Englewood Cliffs, NJ.
[36]	Unlocking Game-Changing Wireless Capabilities: Cisco and SITA help Copenhagen Airport Develop New Services for Transforming the Passenger Experience, Customer case study, CISCO (2012). http://www.cisco.com/en/US/prod/collateral/wireless/c36_696714_00_copenhagen_airport_cs.pdf.
[37]	Friedman M Benjamin, Monetary Policy, National Bureau of Economic Research (NBER) Working Paper 8057, October 2000, https://www.nber.org/papers/w8057.pdf.
[38]	Friedman M, The Role of Monetary Policy, American Economic Review, 58: 1-17.

Cite This Article

Plain Text BibTeX RIS

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

Copy | Download

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

Copy | Download

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

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

Download PDF

Sections

Plain Text BibTeX RIS

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

Copy | Download

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

Copy | Download

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