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Effects of Gross Domestic Product and Inflation Rate on Unemployment Rate in Ghana: Comparative Analysis of Multiple Regression and Covariance Matrix Models

Received: 4 February 2019     Accepted: 11 March 2019     Published: 22 April 2019
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Abstract

This paper analyses the effects of Gross Domestic Product growth (GDP) and Inflation rate (INF) on Unemployment rate (UMP) in Ghana’s economy using covariance matrix and multiple regression models. The two models were examined separately on the same data of three variables and the different outputs analysed to determine the effectiveness among the two models. The analyses of the outputs highlight the significance of both predictor variables on unemployment rate in Ghana. Scatterplot and normal probability distribution (pnorm) graphs were used to analyse the normality of the predictor variables. Data on inflation rate and GDP growth spanning from 1991 to 2017 was used. The data was transformed to n X m matrix form for covariance –variance matrix analysis. The rows in the n by m data matrix were the multivariate observations on n units. Multiple regression analysis was performed on the data. Both the two methods provided the long-run effects of the two predictor variables on the unemployment rate. However, while multiple regression model could quantify the effect of each predictor variable on the predicted variable, the covariance matrix model only quantifies the relation existing between predictor variables and the predicted variable.

Published in American Journal of Applied Mathematics (Volume 7, Issue 1)
DOI 10.11648/j.ajam.20190701.12
Page(s) 5-12
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), 2019. Published by Science Publishing Group

Keywords

Gross Domestic Product Growth Rate, Inflation Rate, Unemployment Rate, Covariance Matrix Model, Multiple Regression Model, Scatterplot Graphs and Normal Probability Distribution (Pnorm)

References
[1] Chang-Shuai, L and Zi-juan, L. (2012), Study on the Relationship among Chinese Unemployment Rate, Economic Growth and Inflation, Advances in Applied Economics and Finance, Vol. 1, pp. 1- 6.
[2] Friedman, M. (1976), Inflation and Unemployment. Chicago Journals - Journal of Political Economy, pp. 451 - 472.
[3] Knoema, Y. (2017), rate of Unemployment, GDP and Inflation in Ghana, https://knoema.com/atlas/Ghana/Unemployment-rate. Accessed: August 20, 2018.
[4] Muhammad, U. and Raza, M. U. (2013). Impact of GDP and Inflation on Unemployment Rate: A. International Review of Management and Business Research, Vol. 2, Issue 2. pp. 388-400.
[5] Metin, K. (1998). The Relationship between Inflation and the Budget Deficit in Turkey. Journal of Business & Economic Statistics, Vol. 16, No. 4, pp. 412-422.
[6] Mohsenia, M. and Jouzaryan, F. (2016), Examining the Effects of Inflation and Unemployment on Economic Growth in Iran. Procedia Economics and Finance, Vol. 36, pp. 381-389.
[7] Mocan, H. N (1995). Income Inequality, Poverty, and Macroeconomic Conditions. paper presented at the American Economic Association Meetings, Washington, D. C.
[8] Newey, W K. and West, KD., 1994. Automatic Lag Selection in Covariance Matrix Estimation. Review of Economic Studies, 61 (4), pp. 631–654.
[9] Raza, M. U. (2013). Impact of GDP and Inflation on Unemployment Rate: A. International Review of Management and Business Research, pp. 388-400.
[10] Shahid, M. (2014), 'Effects of inflation and unemployment on economic growth in Pakistan', Journal of economics and sustainable development, Vol. 5, pp. 13-15.
[11] Yelwa, M., David, K. and Awe, E. O. (2015), Analysis of the Relationship between Inflation, Unemployment and Economic Growth in Nigeria: http://www.imf.org/external/pubs/ft/weo/2016/02/weodata/weoselgr.aspx
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  • APA Style

    Brew Lewis, Crankson Monica Veronica, Nyarko Francis, Ampofi Isaac. (2019). Effects of Gross Domestic Product and Inflation Rate on Unemployment Rate in Ghana: Comparative Analysis of Multiple Regression and Covariance Matrix Models. American Journal of Applied Mathematics, 7(1), 5-12. https://doi.org/10.11648/j.ajam.20190701.12

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    ACS Style

    Brew Lewis; Crankson Monica Veronica; Nyarko Francis; Ampofi Isaac. Effects of Gross Domestic Product and Inflation Rate on Unemployment Rate in Ghana: Comparative Analysis of Multiple Regression and Covariance Matrix Models. Am. J. Appl. Math. 2019, 7(1), 5-12. doi: 10.11648/j.ajam.20190701.12

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    AMA Style

    Brew Lewis, Crankson Monica Veronica, Nyarko Francis, Ampofi Isaac. Effects of Gross Domestic Product and Inflation Rate on Unemployment Rate in Ghana: Comparative Analysis of Multiple Regression and Covariance Matrix Models. Am J Appl Math. 2019;7(1):5-12. doi: 10.11648/j.ajam.20190701.12

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  • @article{10.11648/j.ajam.20190701.12,
      author = {Brew Lewis and Crankson Monica Veronica and Nyarko Francis and Ampofi Isaac},
      title = {Effects of Gross Domestic Product and Inflation Rate on Unemployment Rate in Ghana: Comparative Analysis of Multiple Regression and Covariance Matrix Models},
      journal = {American Journal of Applied Mathematics},
      volume = {7},
      number = {1},
      pages = {5-12},
      doi = {10.11648/j.ajam.20190701.12},
      url = {https://doi.org/10.11648/j.ajam.20190701.12},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajam.20190701.12},
      abstract = {This paper analyses the effects of Gross Domestic Product growth (GDP) and Inflation rate (INF) on Unemployment rate (UMP) in Ghana’s economy using covariance matrix and multiple regression models. The two models were examined separately on the same data of three variables and the different outputs analysed to determine the effectiveness among the two models. The analyses of the outputs highlight the significance of both predictor variables on unemployment rate in Ghana. Scatterplot and normal probability distribution (pnorm) graphs were used to analyse the normality of the predictor variables. Data on inflation rate and GDP growth spanning from 1991 to 2017 was used. The data was transformed to n X m matrix form for covariance –variance matrix analysis. The rows in the n by m data matrix were the multivariate observations on n units. Multiple regression analysis was performed on the data. Both the two methods provided the long-run effects of the two predictor variables on the unemployment rate. However, while multiple regression model could quantify the effect of each predictor variable on the predicted variable, the covariance matrix model only quantifies the relation existing between predictor variables and the predicted variable.},
     year = {2019}
    }
    

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    AU  - Crankson Monica Veronica
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    AB  - This paper analyses the effects of Gross Domestic Product growth (GDP) and Inflation rate (INF) on Unemployment rate (UMP) in Ghana’s economy using covariance matrix and multiple regression models. The two models were examined separately on the same data of three variables and the different outputs analysed to determine the effectiveness among the two models. The analyses of the outputs highlight the significance of both predictor variables on unemployment rate in Ghana. Scatterplot and normal probability distribution (pnorm) graphs were used to analyse the normality of the predictor variables. Data on inflation rate and GDP growth spanning from 1991 to 2017 was used. The data was transformed to n X m matrix form for covariance –variance matrix analysis. The rows in the n by m data matrix were the multivariate observations on n units. Multiple regression analysis was performed on the data. Both the two methods provided the long-run effects of the two predictor variables on the unemployment rate. However, while multiple regression model could quantify the effect of each predictor variable on the predicted variable, the covariance matrix model only quantifies the relation existing between predictor variables and the predicted variable.
    VL  - 7
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Author Information
  • Department of Mathematical Sciences, University of Mines and Technology, Tarkwa, Ghana

  • Department of Mathematical Sciences, University of Mines and Technology, Tarkwa, Ghana

  • Human Resource Unit, University of Mines and Technology, Tarkwa, Ghana

  • Department of Mathematical Sciences, University of Mines and Technology, Tarkwa, Ghana

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