Comparing Two Classical Methods of Detecting Multicollinearity in Financial and Economic Time Series Data

Mutairu Oyewale Akintunde; Abolade Oludayo Olawale; Ajitoni Simeon Amusan; Adeyinka Ismail Abdul Azeez

doi:doi:10.11648/j.ijamtp.20210703.11

| Peer-Reviewed

Comparing Two Classical Methods of Detecting Multicollinearity in Financial and Economic Time Series Data

Mutairu Oyewale Akintunde, Abolade Oludayo Olawale, Ajitoni Simeon Amusan, Adeyinka Ismail Abdul Azeez

Published in International Journal of Applied Mathematics and Theoretical Physics (Volume 7, Issue 3)

Received: 12 May 2021 Accepted: 7 June 2021 Published: 18 August 2021

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

Abstract

Multicollinearity is an unavoidable problem being faced by researchers in financial and Economic data. It refers to a situation where the degrees of correlations between two or more independent variables are high. This is to say, one explanatory variable can be used in forecasting the other variable. This creates redundant information in a series under study, skewing the results in regression models. There is need to search for the source of the problem and proffering solution to this problem in Economics and Financial data. The data used was extracted from the record of Federal trade commission (FTC), 2019. The commission usually ranks annually arrays of locally made cigarettes in relation to Tar, nicotine and carbon monoxide components that was made available. Farrah-Glauber test and variance inflation factor were used as methods of detection multicollinearity in this paper. SPSS and J-muliti packages were used to analyse the data collected for empirical illustration. The results of analysis indicated that variance inflation factor of X₁ and X₂ (Tar and Nicotine) are far above 10 (21.63 and 21.90) must be removed or collapsed from the model in order to correct multicollinearity. So, the preciseness of VIF made it to be preferred to Farrah-Glauber test. In line with the analysis, the use of Variance Inflation Factor is more preferred to Farrah-Glauber method. As VIF not only detected but also pointed to the direction of the problem.

Published in	International Journal of Applied Mathematics and Theoretical Physics (Volume 7, Issue 3)
DOI	10.11648/j.ijamtp.20210703.11
Page(s)	62-67
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

Multicollinearity, Farrah-Glauber, Predictor, Variance Inflation Factor, Financial and Economic Data, Regression Model

References

[1]	Alin, A. 2010. Multicollinearity. Journal of WIREs Computational Statistics How does Climate Change affect biodiversity, Vol. 2 No. 3.
[2]	Ayinde, K; Lukman, AF; Arowolo, OT (2015). Combined parameters estimation methods of linear regression model with multicollinearity and autocorrelation. Journal of Asian Scientific Research 5 (5), 243–250.
[3]	Belsley, D. A. 1991. Conditioning Diagnostics: Collinearity and Weak Data Regression. John Wiley, New York.
[4]	Chatfield, C. 1995. Model uncertainty, data mining and statistical inference. Journal of Royal Statistical Society Vol. 158, No. 3: 419-466.
[5]	Graham, M. H. 2003. Confronting multicollinearity in ecological multiple regression. journal of Ecology society of America Vol. 84, No. 11: 2809-2815.
[6]	Goldberger, Arthur, 1991. A course in Econometrics (Cambridge, MA: Harvard University Press).
[7]	Greene, William H., 2003. Econometric Analysis (5th ed. Upper Saddle River, NJ: Prentice Hall).
[8]	Ismail, B; Manjula, S (2016). Estimation of linear regression model with correlated regressors in the presence of autocorrelation. International Journal of Statistics and Applications 6 (2), 35–39.
[9]	Kalnins, Arturs, 2018. “Multicollinearity: How common factors cause Type 1 errors in multivariate regression,” Strategic Management Journal 22, issue 10.
[10]	Kiers, H. A. L. and Smilde, A. K. 2007. A comparison of various methods for multivariate regression with highly collinear variables. Journal of Statistical Methods and Applications Vol. 4, No. 6: 193-228.
[11]	Lukman, AF; Osowole, O. I; Ayinde, K (2015). Two stage robust method in a linear regression model. Journal of Modern Applied Statistical Methods 14 (2), 53–67.
[12]	Meloun, M., Militiky, J. and Hilli, M. 2002. Crucial problems in regression modelling and their solutions. Journal of Analyst Vol. 12, No. 7: 433-450.
[13]	Mikolajczyk, DiSilvestro A, Zhang J. 2008. Evaluation of logistic regression reporting in current obstetrics and gynecology literature. Journal of Obstetrics & Gynecology Vol. 111 No. 10: 413-419.
[14]	Murray, C. J. L., Kulkarni SC, Michaud C, Tomijima N. and Bulzacchelli. MT, 2006. Eight Americas: Investigating mortality disparity across races, counties, and race-counties in the United States. - PLoS Medicine 3: e260.
[15]	Olanrewaju, S. O.; Yahaya, H. U. and Nasiru, M. O., (2017). Effects of multicollinearity on some estimators in a system of regression equation. European Journal of Statistics and Probability 5 (3), 1–15.
[16]	Smith, A. C., Nicola, K., Charles, M. F. and Lenore, F. 2009. Confronting collinearity: comparing methods for disentangling the effects of habitat loss and fragmentation. Journal of Landscape Ecology Vol. 24, No. 2: 1271-1285.
[17]	Stewart, G. W. (1987). Collinearity and least squares regression. Statistical Science, 68–84.
[18]	Tyagi, G; Chandra, S (2017). Note on the performance of biased estimators with autocorrelated errors. International Journal of Mathematics and Mathematical Sciences. Volume 2017, 12 Pages, Article ID 2045653.
[19]	Wooldridge, J., (2010). Econometric Analysis of Cross Section and Panel Data. The MIT Press, Cambridge, Mass.

Cite This Article

Plain Text BibTeX RIS

APA Style

Mutairu Oyewale Akintunde, Abolade Oludayo Olawale, Ajitoni Simeon Amusan, Adeyinka Ismail Abdul Azeez. (2021). Comparing Two Classical Methods of Detecting Multicollinearity in Financial and Economic Time Series Data. International Journal of Applied Mathematics and Theoretical Physics, 7(3), 62-67. https://doi.org/10.11648/j.ijamtp.20210703.11

Copy | Download

ACS Style

Mutairu Oyewale Akintunde; Abolade Oludayo Olawale; Ajitoni Simeon Amusan; Adeyinka Ismail Abdul Azeez. Comparing Two Classical Methods of Detecting Multicollinearity in Financial and Economic Time Series Data. Int. J. Appl. Math. Theor. Phys. 2021, 7(3), 62-67. doi: 10.11648/j.ijamtp.20210703.11

Copy | Download

AMA Style

Mutairu Oyewale Akintunde, Abolade Oludayo Olawale, Ajitoni Simeon Amusan, Adeyinka Ismail Abdul Azeez. Comparing Two Classical Methods of Detecting Multicollinearity in Financial and Economic Time Series Data. Int J Appl Math Theor Phys. 2021;7(3):62-67. doi: 10.11648/j.ijamtp.20210703.11

Copy | Download

@article{10.11648/j.ijamtp.20210703.11,
  author = {Mutairu Oyewale Akintunde and Abolade Oludayo Olawale and Ajitoni Simeon Amusan and Adeyinka Ismail Abdul Azeez},
  title = {Comparing Two Classical Methods of Detecting Multicollinearity in Financial and Economic Time Series Data},
  journal = {International Journal of Applied Mathematics and Theoretical Physics},
  volume = {7},
  number = {3},
  pages = {62-67},
  doi = {10.11648/j.ijamtp.20210703.11},
  url = {https://doi.org/10.11648/j.ijamtp.20210703.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijamtp.20210703.11},
  abstract = {Multicollinearity is an unavoidable problem being faced by researchers in financial and Economic data. It refers to a situation where the degrees of correlations between two or more independent variables are high. This is to say, one explanatory variable can be used in forecasting the other variable. This creates redundant information in a series under study, skewing the results in regression models. There is need to search for the source of the problem and proffering solution to this problem in Economics and Financial data. The data used was extracted from the record of Federal trade commission (FTC), 2019. The commission usually ranks annually arrays of locally made cigarettes in relation to Tar, nicotine and carbon monoxide components that was made available. Farrah-Glauber test and variance inflation factor were used as methods of detection multicollinearity in this paper. SPSS and J-muliti packages were used to analyse the data collected for empirical illustration. The results of analysis indicated that variance inflation factor of X1 and X2 (Tar and Nicotine) are far above 10 (21.63 and 21.90) must be removed or collapsed from the model in order to correct multicollinearity. So, the preciseness of VIF made it to be preferred to Farrah-Glauber test. In line with the analysis, the use of Variance Inflation Factor is more preferred to Farrah-Glauber method. As VIF not only detected but also pointed to the direction of the problem.},
 year = {2021}
}

Copy | Download

TY - JOUR
T1 - Comparing Two Classical Methods of Detecting Multicollinearity in Financial and Economic Time Series Data
AU - Mutairu Oyewale Akintunde
AU - Abolade Oludayo Olawale
AU - Ajitoni Simeon Amusan
AU - Adeyinka Ismail Abdul Azeez
Y1 - 2021/08/18
PY - 2021
N1 - https://doi.org/10.11648/j.ijamtp.20210703.11
DO - 10.11648/j.ijamtp.20210703.11
T2 - International Journal of Applied Mathematics and Theoretical Physics
JF - International Journal of Applied Mathematics and Theoretical Physics
JO - International Journal of Applied Mathematics and Theoretical Physics
SP - 62
EP - 67
PB - Science Publishing Group
SN - 2575-5927
UR - https://doi.org/10.11648/j.ijamtp.20210703.11
AB - Multicollinearity is an unavoidable problem being faced by researchers in financial and Economic data. It refers to a situation where the degrees of correlations between two or more independent variables are high. This is to say, one explanatory variable can be used in forecasting the other variable. This creates redundant information in a series under study, skewing the results in regression models. There is need to search for the source of the problem and proffering solution to this problem in Economics and Financial data. The data used was extracted from the record of Federal trade commission (FTC), 2019. The commission usually ranks annually arrays of locally made cigarettes in relation to Tar, nicotine and carbon monoxide components that was made available. Farrah-Glauber test and variance inflation factor were used as methods of detection multicollinearity in this paper. SPSS and J-muliti packages were used to analyse the data collected for empirical illustration. The results of analysis indicated that variance inflation factor of X1 and X2 (Tar and Nicotine) are far above 10 (21.63 and 21.90) must be removed or collapsed from the model in order to correct multicollinearity. So, the preciseness of VIF made it to be preferred to Farrah-Glauber test. In line with the analysis, the use of Variance Inflation Factor is more preferred to Farrah-Glauber method. As VIF not only detected but also pointed to the direction of the problem.
VL - 7
IS - 3
ER -

Copy | Download

Author Information

Mutairu Oyewale Akintunde

Department of Statistics, School of Applied Sciences, Federal Polytechnic, Ede, Nigeria
Abolade Oludayo Olawale

Department of Statistics, School of Applied Sciences, Federal Polytechnic, Ede, Nigeria
Ajitoni Simeon Amusan

Department of Statistics, School of Applied Sciences, Federal Polytechnic, Ede, Nigeria
Adeyinka Ismail Abdul Azeez

Department of Statistics, School of Applied Sciences, Federal Polytechnic, Ede, Nigeria

Download PDF

Sections

Plain Text BibTeX RIS

APA Style

Mutairu Oyewale Akintunde, Abolade Oludayo Olawale, Ajitoni Simeon Amusan, Adeyinka Ismail Abdul Azeez. (2021). Comparing Two Classical Methods of Detecting Multicollinearity in Financial and Economic Time Series Data. International Journal of Applied Mathematics and Theoretical Physics, 7(3), 62-67. https://doi.org/10.11648/j.ijamtp.20210703.11

Copy | Download

ACS Style

Mutairu Oyewale Akintunde; Abolade Oludayo Olawale; Ajitoni Simeon Amusan; Adeyinka Ismail Abdul Azeez. Comparing Two Classical Methods of Detecting Multicollinearity in Financial and Economic Time Series Data. Int. J. Appl. Math. Theor. Phys. 2021, 7(3), 62-67. doi: 10.11648/j.ijamtp.20210703.11

Copy | Download

AMA Style

Mutairu Oyewale Akintunde, Abolade Oludayo Olawale, Ajitoni Simeon Amusan, Adeyinka Ismail Abdul Azeez. Comparing Two Classical Methods of Detecting Multicollinearity in Financial and Economic Time Series Data. Int J Appl Math Theor Phys. 2021;7(3):62-67. doi: 10.11648/j.ijamtp.20210703.11

Copy | Download

@article{10.11648/j.ijamtp.20210703.11,
  author = {Mutairu Oyewale Akintunde and Abolade Oludayo Olawale and Ajitoni Simeon Amusan and Adeyinka Ismail Abdul Azeez},
  title = {Comparing Two Classical Methods of Detecting Multicollinearity in Financial and Economic Time Series Data},
  journal = {International Journal of Applied Mathematics and Theoretical Physics},
  volume = {7},
  number = {3},
  pages = {62-67},
  doi = {10.11648/j.ijamtp.20210703.11},
  url = {https://doi.org/10.11648/j.ijamtp.20210703.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijamtp.20210703.11},
  abstract = {Multicollinearity is an unavoidable problem being faced by researchers in financial and Economic data. It refers to a situation where the degrees of correlations between two or more independent variables are high. This is to say, one explanatory variable can be used in forecasting the other variable. This creates redundant information in a series under study, skewing the results in regression models. There is need to search for the source of the problem and proffering solution to this problem in Economics and Financial data. The data used was extracted from the record of Federal trade commission (FTC), 2019. The commission usually ranks annually arrays of locally made cigarettes in relation to Tar, nicotine and carbon monoxide components that was made available. Farrah-Glauber test and variance inflation factor were used as methods of detection multicollinearity in this paper. SPSS and J-muliti packages were used to analyse the data collected for empirical illustration. The results of analysis indicated that variance inflation factor of X1 and X2 (Tar and Nicotine) are far above 10 (21.63 and 21.90) must be removed or collapsed from the model in order to correct multicollinearity. So, the preciseness of VIF made it to be preferred to Farrah-Glauber test. In line with the analysis, the use of Variance Inflation Factor is more preferred to Farrah-Glauber method. As VIF not only detected but also pointed to the direction of the problem.},
 year = {2021}
}

Copy | Download