Modeling Macroeconomic Variables Using Principal Component Analysis and Multiple Linear Regression: The Case of Ghana’s Economy

Eyiah-bediako Francis; Bosson-amedenu Senyefia; Otoo Joseph

doi:doi:10.11648/j.jbed.20200501.11

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Modeling Macroeconomic Variables Using Principal Component Analysis and Multiple Linear Regression: The Case of Ghana’s Economy

Eyiah-bediako Francis, Bosson-amedenu Senyefia, Otoo Joseph

Published in Journal of Business and Economic Development (Volume 5, Issue 1)

Received: 25 December 2019 Accepted: 4 January 2020 Published: 13 January 2020

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Abstract

The paper sought to model the relationship between GDP and 29 macroeconomic variables in Ghana using the Principal Component Analysis and multiple linear regression. Economic data with 583 data points were collected from January, 1990 through to May, 2018. The KMO statistics was 0.750 and the Bartlett's Test of sphericity statistic obtained for the data was 24807.231 of p-value 0.000. The variables were found to be powerfully correlated with reference to the correlation matrix. Principal Component Analysis was performed to reduce the factors (using orthogonal varimax technique to produce uncorrelated factor structures to help allocate appropriately loadings to factors) to a minimum without compromising the variability of the original data. Seven factors were retained (explained 74% of the overall variation) after using multiple extraction approaches of Scree test, Kaiser Criterion and parallel analysis to avoid over- and under-extraction errors. Regression analysis was performed where component scores were used to develop a relationship with the uncorrelated components and GDP. The component 2 (Closed Economy without Government Activities) explicitly contained seven indicators consisting of consumer price index-Food, Consumer price index-Nonfood, Consumer Price index (overall), Monetary Policy Rate, 91-Days Treasury Bill, 182-Days Treasury Bill, crude oil, and Core Inflation (Adjusted for Energy and Utility). Component 2 was significant and positively related with GDP (B = 0.6, p<0.01). Again, Component 5 (Closed Economy with Government activities) explicitly contained two indicators such as Tax-Equivalent Rate on 28-Days Treasury Bill and Tax-Equivalent Rate on 56-DaysTreasury Bill. Component 5 had a positive and significant impact on GDP (B = 0.386, p<0.01). However, component 4 (monetary economy; B = -3.927, p<0.01), component 6 (B = -0.577, p<0.01) and component 7 (B = -0.256, p<0.01) were negatively related with GDP but were statistically significant. The R-squared value of 0.304 shows that the regression model explains about 30% of the variance. It was recommended for future researchers to consider increasing the number of macroeconomic variables to increase the predictive power of the model.

Published in	Journal of Business and Economic Development (Volume 5, Issue 1)
DOI	10.11648/j.jbed.20200501.11
Page(s)	1-9
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

Principal Component Analysis, Modeling, Macroeconimic Economic Variables, Ghana, Factor Analysis, Eigenvalues, Multiple Linear Regression

References

[1]	Armeanu D, Lache L. (2018): Application of the Model of Principal Components Analysis on Romanian Insurance Market, Theoretical and Applied Economics.
[2]	Fan J, Sun Q, Wen-Xin Z, and Ziwei Z. (2018): Principal component analysis for big data arXiv: 1801.01602v1 [stat. ME].
[3]	Syeda F, Muhammad S. and Shah G. A. A (2013). Effects of Macroeconomic Variables on Gross Domestic Product (GDP) in Pakistan. International Conference on Applied Economics (ICOAE) 2013. Procedia Economics and Finance 5 (2013) 703–711.
[4]	Hussain A, Hazoor M. Sabir and Kashif M (2016). Impact of macroeconomic variables on GDP: evidence from Pakistan European Journal of Business and Innovation Research Vol. 4, No. 3, pp. 38-52, June 2016.
[5]	Purnamasari D. U, Surawidarto M., Dedek Hadi A. S, and Istiyono E., (2019), “Exploratory Factor Analysis: Motivation for Learning” in The FirstInternational Conferenceon Education, Science and Training: Empowering Educational Human Resources for Global Competitiveness, KnE Social Sciences, pages58–65. DOI10.18502/kss.v3i15.4354.
[6]	Adongo F. A, John Amo Jr. L, Chikelu C. J, Osei M, (2018): Principal Component and Factor Analysis of Macroeconomic Indicators, IOSR Journal Of Humanities And Social Science (IOSR-JHSS) Volume 23, Issue 7, Ver. 10 (July. 2018) PP 01-07 e-ISSN: 2279-0837, p-ISSN: 2279-0845. www.iosrjournals.org.
[7]	Razzak H, Ali M, (2015): Principal Component Analysis Of Socioeconomic Factors And Their Association With Life Expectancy At Birth In Asia, International Journal of Multidisciplinary Academic Research Vol. 3, No. 1, 2015, ISSN 2309-3218.
[8]	Twenefour F. B. K., Nortey E. N. N., Baah, E. M. (2015): Principal Component Analysis of Students Academic Performance International Journal of Business and Social Research, Volume 05, Issue 02, 2015.
[9]	Field, A. P. (2005). Discovering statistics using SPSS (2nd edition). London: Sage.
[10]	Scott F. B., Gibson, David J., Robertson, Philip A., Pohlmann, John T. and Fralish, James S. (1995): "Parallel Analysis: a Method for Determining Significant Principal Components." (Feb 1995).
[11]	Williams B. (2012): Exploratory factor analysis: A five-step guide for novices. Australasian Journal of Paramedicine Volume 8 \| Issue 3.
[12]	Hans P. F & Janssens E. (2019). Spurious principal components, Applied Economics Letters, 26: 1, 37-39, DOI: 10.1080/13504851.2018.1433292.
[13]	Schwarz J. (2011): Research Methodology: Tools, Applied Data Analysis (with SPSS), Lecture 03: Factor Analysis.
[14]	Mehmedinovic S. (2017) Fundamentals Of Application Factor Analysis In Education And Rehabilitation, DOI: 10.21554/hrr.041708.
[15]	Taherdoost H., Sahibuddin S., Jalaliyoon N. (2010) Exploratory Factor Analysis; Concepts and Theory, Advances in Applied and Pure Mathematics ISBN: 978-960-474-380-3.

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APA Style

Eyiah-bediako Francis, Bosson-amedenu Senyefia, Otoo Joseph. (2020). Modeling Macroeconomic Variables Using Principal Component Analysis and Multiple Linear Regression: The Case of Ghana’s Economy. Journal of Business and Economic Development, 5(1), 1-9. https://doi.org/10.11648/j.jbed.20200501.11

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ACS Style

Eyiah-bediako Francis; Bosson-amedenu Senyefia; Otoo Joseph. Modeling Macroeconomic Variables Using Principal Component Analysis and Multiple Linear Regression: The Case of Ghana’s Economy. J. Bus. Econ. Dev. 2020, 5(1), 1-9. doi: 10.11648/j.jbed.20200501.11

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AMA Style

Eyiah-bediako Francis, Bosson-amedenu Senyefia, Otoo Joseph. Modeling Macroeconomic Variables Using Principal Component Analysis and Multiple Linear Regression: The Case of Ghana’s Economy. J Bus Econ Dev. 2020;5(1):1-9. doi: 10.11648/j.jbed.20200501.11

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@article{10.11648/j.jbed.20200501.11,
  author = {Eyiah-bediako Francis and Bosson-amedenu Senyefia and Otoo Joseph},
  title = {Modeling Macroeconomic Variables Using Principal Component Analysis and Multiple Linear Regression: The Case of Ghana’s Economy},
  journal = {Journal of Business and Economic Development},
  volume = {5},
  number = {1},
  pages = {1-9},
  doi = {10.11648/j.jbed.20200501.11},
  url = {https://doi.org/10.11648/j.jbed.20200501.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jbed.20200501.11},
  abstract = {The paper sought to model the relationship between GDP and 29 macroeconomic variables in Ghana using the Principal Component Analysis and multiple linear regression. Economic data with 583 data points were collected from January, 1990 through to May, 2018. The KMO statistics was 0.750 and the Bartlett's Test of sphericity statistic obtained for the data was 24807.231 of p-value 0.000. The variables were found to be powerfully correlated with reference to the correlation matrix. Principal Component Analysis was performed to reduce the factors (using orthogonal varimax technique to produce uncorrelated factor structures to help allocate appropriately loadings to factors) to a minimum without compromising the variability of the original data. Seven factors were retained (explained 74% of the overall variation) after using multiple extraction approaches of Scree test, Kaiser Criterion and parallel analysis to avoid over- and under-extraction errors. Regression analysis was performed where component scores were used to develop a relationship with the uncorrelated components and GDP. The component 2 (Closed Economy without Government Activities) explicitly contained seven indicators consisting of consumer price index-Food, Consumer price index-Nonfood, Consumer Price index (overall), Monetary Policy Rate, 91-Days Treasury Bill, 182-Days Treasury Bill, crude oil, and Core Inflation (Adjusted for Energy and Utility). Component 2 was significant and positively related with GDP (B = 0.6, p<0.01). Again, Component 5 (Closed Economy with Government activities) explicitly contained two indicators such as Tax-Equivalent Rate on 28-Days Treasury Bill and Tax-Equivalent Rate on 56-DaysTreasury Bill. Component 5 had a positive and significant impact on GDP (B = 0.386, p<0.01). However, component 4 (monetary economy; B = -3.927, p<0.01), component 6 (B = -0.577, p<0.01) and component 7 (B = -0.256, p<0.01) were negatively related with GDP but were statistically significant. The R-squared value of 0.304 shows that the regression model explains about 30% of the variance. It was recommended for future researchers to consider increasing the number of macroeconomic variables to increase the predictive power of the model.},
 year = {2020}
}

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TY  - JOUR
T1  - Modeling Macroeconomic Variables Using Principal Component Analysis and Multiple Linear Regression: The Case of Ghana’s Economy
AU  - Eyiah-bediako Francis
AU  - Bosson-amedenu Senyefia
AU  - Otoo Joseph
Y1  - 2020/01/13
PY  - 2020
N1  - https://doi.org/10.11648/j.jbed.20200501.11
DO  - 10.11648/j.jbed.20200501.11
T2  - Journal of Business and Economic Development
JF  - Journal of Business and Economic Development
JO  - Journal of Business and Economic Development
SP  - 1
EP  - 9
PB  - Science Publishing Group
SN  - 2637-3874
UR  - https://doi.org/10.11648/j.jbed.20200501.11
AB  - The paper sought to model the relationship between GDP and 29 macroeconomic variables in Ghana using the Principal Component Analysis and multiple linear regression. Economic data with 583 data points were collected from January, 1990 through to May, 2018. The KMO statistics was 0.750 and the Bartlett's Test of sphericity statistic obtained for the data was 24807.231 of p-value 0.000. The variables were found to be powerfully correlated with reference to the correlation matrix. Principal Component Analysis was performed to reduce the factors (using orthogonal varimax technique to produce uncorrelated factor structures to help allocate appropriately loadings to factors) to a minimum without compromising the variability of the original data. Seven factors were retained (explained 74% of the overall variation) after using multiple extraction approaches of Scree test, Kaiser Criterion and parallel analysis to avoid over- and under-extraction errors. Regression analysis was performed where component scores were used to develop a relationship with the uncorrelated components and GDP. The component 2 (Closed Economy without Government Activities) explicitly contained seven indicators consisting of consumer price index-Food, Consumer price index-Nonfood, Consumer Price index (overall), Monetary Policy Rate, 91-Days Treasury Bill, 182-Days Treasury Bill, crude oil, and Core Inflation (Adjusted for Energy and Utility). Component 2 was significant and positively related with GDP (B = 0.6, p<0.01). Again, Component 5 (Closed Economy with Government activities) explicitly contained two indicators such as Tax-Equivalent Rate on 28-Days Treasury Bill and Tax-Equivalent Rate on 56-DaysTreasury Bill. Component 5 had a positive and significant impact on GDP (B = 0.386, p<0.01). However, component 4 (monetary economy; B = -3.927, p<0.01), component 6 (B = -0.577, p<0.01) and component 7 (B = -0.256, p<0.01) were negatively related with GDP but were statistically significant. The R-squared value of 0.304 shows that the regression model explains about 30% of the variance. It was recommended for future researchers to consider increasing the number of macroeconomic variables to increase the predictive power of the model.
VL  - 5
IS  - 1
ER  -

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Author Information

Eyiah-bediako Francis

1Department of Statistics, University of Cape Coast, Cape Coast, Ghana
Bosson-amedenu Senyefia

Department of Mathematics and ICT, Holy Child College of Education, Takoradi, Ghana
Otoo Joseph

Department of Statistics and Actuarial Science, University of Ghana, Legon, Greater Accra, Ghana

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APA Style

Eyiah-bediako Francis, Bosson-amedenu Senyefia, Otoo Joseph. (2020). Modeling Macroeconomic Variables Using Principal Component Analysis and Multiple Linear Regression: The Case of Ghana’s Economy. Journal of Business and Economic Development, 5(1), 1-9. https://doi.org/10.11648/j.jbed.20200501.11

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ACS Style

Eyiah-bediako Francis; Bosson-amedenu Senyefia; Otoo Joseph. Modeling Macroeconomic Variables Using Principal Component Analysis and Multiple Linear Regression: The Case of Ghana’s Economy. J. Bus. Econ. Dev. 2020, 5(1), 1-9. doi: 10.11648/j.jbed.20200501.11

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AMA Style

Eyiah-bediako Francis, Bosson-amedenu Senyefia, Otoo Joseph. Modeling Macroeconomic Variables Using Principal Component Analysis and Multiple Linear Regression: The Case of Ghana’s Economy. J Bus Econ Dev. 2020;5(1):1-9. doi: 10.11648/j.jbed.20200501.11

Copy | Download

@article{10.11648/j.jbed.20200501.11,
  author = {Eyiah-bediako Francis and Bosson-amedenu Senyefia and Otoo Joseph},
  title = {Modeling Macroeconomic Variables Using Principal Component Analysis and Multiple Linear Regression: The Case of Ghana’s Economy},
  journal = {Journal of Business and Economic Development},
  volume = {5},
  number = {1},
  pages = {1-9},
  doi = {10.11648/j.jbed.20200501.11},
  url = {https://doi.org/10.11648/j.jbed.20200501.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jbed.20200501.11},
  abstract = {The paper sought to model the relationship between GDP and 29 macroeconomic variables in Ghana using the Principal Component Analysis and multiple linear regression. Economic data with 583 data points were collected from January, 1990 through to May, 2018. The KMO statistics was 0.750 and the Bartlett's Test of sphericity statistic obtained for the data was 24807.231 of p-value 0.000. The variables were found to be powerfully correlated with reference to the correlation matrix. Principal Component Analysis was performed to reduce the factors (using orthogonal varimax technique to produce uncorrelated factor structures to help allocate appropriately loadings to factors) to a minimum without compromising the variability of the original data. Seven factors were retained (explained 74% of the overall variation) after using multiple extraction approaches of Scree test, Kaiser Criterion and parallel analysis to avoid over- and under-extraction errors. Regression analysis was performed where component scores were used to develop a relationship with the uncorrelated components and GDP. The component 2 (Closed Economy without Government Activities) explicitly contained seven indicators consisting of consumer price index-Food, Consumer price index-Nonfood, Consumer Price index (overall), Monetary Policy Rate, 91-Days Treasury Bill, 182-Days Treasury Bill, crude oil, and Core Inflation (Adjusted for Energy and Utility). Component 2 was significant and positively related with GDP (B = 0.6, p<0.01). Again, Component 5 (Closed Economy with Government activities) explicitly contained two indicators such as Tax-Equivalent Rate on 28-Days Treasury Bill and Tax-Equivalent Rate on 56-DaysTreasury Bill. Component 5 had a positive and significant impact on GDP (B = 0.386, p<0.01). However, component 4 (monetary economy; B = -3.927, p<0.01), component 6 (B = -0.577, p<0.01) and component 7 (B = -0.256, p<0.01) were negatively related with GDP but were statistically significant. The R-squared value of 0.304 shows that the regression model explains about 30% of the variance. It was recommended for future researchers to consider increasing the number of macroeconomic variables to increase the predictive power of the model.},
 year = {2020}
}

Copy | Download

TY  - JOUR
T1  - Modeling Macroeconomic Variables Using Principal Component Analysis and Multiple Linear Regression: The Case of Ghana’s Economy
AU  - Eyiah-bediako Francis
AU  - Bosson-amedenu Senyefia
AU  - Otoo Joseph
Y1  - 2020/01/13
PY  - 2020
N1  - https://doi.org/10.11648/j.jbed.20200501.11
DO  - 10.11648/j.jbed.20200501.11
T2  - Journal of Business and Economic Development
JF  - Journal of Business and Economic Development
JO  - Journal of Business and Economic Development
SP  - 1
EP  - 9
PB  - Science Publishing Group
SN  - 2637-3874
UR  - https://doi.org/10.11648/j.jbed.20200501.11
AB  - The paper sought to model the relationship between GDP and 29 macroeconomic variables in Ghana using the Principal Component Analysis and multiple linear regression. Economic data with 583 data points were collected from January, 1990 through to May, 2018. The KMO statistics was 0.750 and the Bartlett's Test of sphericity statistic obtained for the data was 24807.231 of p-value 0.000. The variables were found to be powerfully correlated with reference to the correlation matrix. Principal Component Analysis was performed to reduce the factors (using orthogonal varimax technique to produce uncorrelated factor structures to help allocate appropriately loadings to factors) to a minimum without compromising the variability of the original data. Seven factors were retained (explained 74% of the overall variation) after using multiple extraction approaches of Scree test, Kaiser Criterion and parallel analysis to avoid over- and under-extraction errors. Regression analysis was performed where component scores were used to develop a relationship with the uncorrelated components and GDP. The component 2 (Closed Economy without Government Activities) explicitly contained seven indicators consisting of consumer price index-Food, Consumer price index-Nonfood, Consumer Price index (overall), Monetary Policy Rate, 91-Days Treasury Bill, 182-Days Treasury Bill, crude oil, and Core Inflation (Adjusted for Energy and Utility). Component 2 was significant and positively related with GDP (B = 0.6, p<0.01). Again, Component 5 (Closed Economy with Government activities) explicitly contained two indicators such as Tax-Equivalent Rate on 28-Days Treasury Bill and Tax-Equivalent Rate on 56-DaysTreasury Bill. Component 5 had a positive and significant impact on GDP (B = 0.386, p<0.01). However, component 4 (monetary economy; B = -3.927, p<0.01), component 6 (B = -0.577, p<0.01) and component 7 (B = -0.256, p<0.01) were negatively related with GDP but were statistically significant. The R-squared value of 0.304 shows that the regression model explains about 30% of the variance. It was recommended for future researchers to consider increasing the number of macroeconomic variables to increase the predictive power of the model.
VL  - 5
IS  - 1
ER  -

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