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Factorial Analysis as Tool to Predict the Economic Competitiveness of Mexico

Received: 20 May 2019     Accepted: 12 July 2019     Published: 19 December 2019
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Abstract

In the present research work the essential elements are given to build the Economic Competitiveness Index (ICE) of Mexico in 2015, for which, the technique of factorial analysis of multivariable statistics is used. Of the construction of this indicator, we start with the report presented at the World Economic Forum (WEF) in 2016, in which the variables that must be considered to increase the economic competitiveness of the countries captured. With the development of this indicator, it was possible to predict the effects that technological innovation has on the competitiveness of the country. Added to this, it identifies the limitations that each federal entity has in relation to said concept. The development of this factorial model was done through the programming language R.

Published in Science Journal of Applied Mathematics and Statistics (Volume 7, Issue 6)
DOI 10.11648/j.sjams.20190706.13
Page(s) 112-120
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

Competitiveness, Methodology and Factorial Analysis, Development of Indicator

References
[1] Wackerly, D., Mendenhall, W. & Scheaffer, R. (2010). Mathematic Statistics with Applications. Cengage Learning. United States.
[2] Canavos, G. Probability and Statistics. Applications and Methods. Mc Graw Hill. México 1988.
[3] Mondragón, A. (2002). What are the indicators? Statistics Culture and Geography.
[4] Fuente, S. (2011). Factorial Analysis. Autonomous University of Madrid. Spain.
[5] Vinuesa, P. (2016) Topic 8-Correlation: theory and practice. Autonomus University of Mexico. Mexico.
[6] Ruíz, C. (2008). Mexico: Economic Geography of the innovation. Foreign Trade.
[7] Martínez, M. (2005). The concept of productivity in the economic analysis. Spanish Association of Accounting and Business Administration. Spain.
[8] Aguilar, M. The competitiveness and Indicators of Mexico. UPIICSA 2007.
[9] Cepal. (2016). Productivity and Structural Breaks in Mexico. United Nations. Mexico.
[10] Luna, P. (2005). Information and Technology Systems of Information and the Methodology DEIPHI. University of Sevilla. Spain.
[11] Sarabia, A. (1995). General Theory of Systems. ISDEFE. Spain.
[12] Peña, D. (2002). Mathematics in Social Sciences. Multivariate Analysis of data. University Carlos III of Madrid. Spain.
[13] Cuadras, C. (2012). New Methods of Mutivariate Analysis. CMC Editions. Spain.
[14] Castaño, E. (2005). Introduction to the Analysis of Mutivariate Data in Social Sciences. National University of Colombia. Colombia.
[15] Baillo, A. & Grané, A. 100 Solved Problems in Multivariate Statistics (implemented in Matlab). Delta Publications. Spain 2008.
[16] Carmona, F. (2001). Linear Models. University of Barcelona. Spain.
[17] Alaminos, A., Francés, F., Penalva, C., & Santacreu, O. Multivariate Analysis to Social Sciences I. Distance Indexes, Conglomerates and Factorial Analysis. Pydlos Editions. Ecuador 2015.
Cite This Article
  • APA Style

    Juan Bacilio Guerrero Escamilla, Sócrates López Pérez, Yamile Rangel Martinez. (2019). Factorial Analysis as Tool to Predict the Economic Competitiveness of Mexico. Science Journal of Applied Mathematics and Statistics, 7(6), 112-120. https://doi.org/10.11648/j.sjams.20190706.13

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    ACS Style

    Juan Bacilio Guerrero Escamilla; Sócrates López Pérez; Yamile Rangel Martinez. Factorial Analysis as Tool to Predict the Economic Competitiveness of Mexico. Sci. J. Appl. Math. Stat. 2019, 7(6), 112-120. doi: 10.11648/j.sjams.20190706.13

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    AMA Style

    Juan Bacilio Guerrero Escamilla, Sócrates López Pérez, Yamile Rangel Martinez. Factorial Analysis as Tool to Predict the Economic Competitiveness of Mexico. Sci J Appl Math Stat. 2019;7(6):112-120. doi: 10.11648/j.sjams.20190706.13

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  • @article{10.11648/j.sjams.20190706.13,
      author = {Juan Bacilio Guerrero Escamilla and Sócrates López Pérez and Yamile Rangel Martinez},
      title = {Factorial Analysis as Tool to Predict the Economic Competitiveness of Mexico},
      journal = {Science Journal of Applied Mathematics and Statistics},
      volume = {7},
      number = {6},
      pages = {112-120},
      doi = {10.11648/j.sjams.20190706.13},
      url = {https://doi.org/10.11648/j.sjams.20190706.13},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sjams.20190706.13},
      abstract = {In the present research work the essential elements are given to build the Economic Competitiveness Index (ICE) of Mexico in 2015, for which, the technique of factorial analysis of multivariable statistics is used. Of the construction of this indicator, we start with the report presented at the World Economic Forum (WEF) in 2016, in which the variables that must be considered to increase the economic competitiveness of the countries captured. With the development of this indicator, it was possible to predict the effects that technological innovation has on the competitiveness of the country. Added to this, it identifies the limitations that each federal entity has in relation to said concept. The development of this factorial model was done through the programming language R.},
     year = {2019}
    }
    

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    AB  - In the present research work the essential elements are given to build the Economic Competitiveness Index (ICE) of Mexico in 2015, for which, the technique of factorial analysis of multivariable statistics is used. Of the construction of this indicator, we start with the report presented at the World Economic Forum (WEF) in 2016, in which the variables that must be considered to increase the economic competitiveness of the countries captured. With the development of this indicator, it was possible to predict the effects that technological innovation has on the competitiveness of the country. Added to this, it identifies the limitations that each federal entity has in relation to said concept. The development of this factorial model was done through the programming language R.
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Author Information
  • School of Social Sciences and Humanities, Autonomous University of the State of Hidalgo, Pachuca City, Mexico

  • School of Social Sciences and Humanities, Autonomous University of the State of Hidalgo, Pachuca City, Mexico

  • School of Social Sciences and Humanities, Autonomous University of the State of Hidalgo, Pachuca City, Mexico

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