Impact of the COVID-19 Pandemic on Sales Across Economic Sectors: A Canonical Biplot Analysis in Milagro, Ecuador
Resumen
The article provides a detailed analysis of sales in the San Francisco de
Milagro canton during the period 2017-2023, focusing on economic resilience in
the face of the COVID-19 pandemic. Using data from the Internal Revenue Service
and the International Standard Industrial Classification, the main economic sectors
are identified, and their evolution over time is analyzed. Distinctive patterns in sales
are observed, highlighting the impact of the pandemic on sectors such as Commerce
and Agriculture. The application of Canonical Biplot provides a graphical visualization
of the relationships between sectors and years, revealing complex patterns and
underscoring the importance of promoting trade and agriculture to ensure long-term
economic stability in Ecuador.
Citas
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Chen, H., Qian, W., & Wen, Q. (2021). The impact of the COVID-19 pandemic on consumption: Learning from high-frequency transaction data. Journal of Financial Economics, 142(1), 123-144.
Smith, J., & Williams, K. (2022). Multivariate statistical methods in economic research: Applications and case studies. Econometric Reviews, 41(5), 678-704.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2021). Multivariate Data Analysis. Journal of Marketing Research, 58(1), 1-9.
Zhou, L., Wu, J., & Meng, T. (2023). Post-pandemic economic recovery: A multivariate analysis of sectoral resilience. Economic Modelling, 113, 105824.
Johnson, R. A., & Wichern, D. W. (2022). Applied Multivariate Statistical Analysis. Journal of Applied Statistics, 49(4), 721-738.
Martínez, L., Fernández, A., & González, R. (2022). Sectoral resilience and economic recovery in the post-COVID-19 era. Regional Studies, 56(3), 438-455.
Davis, P., Wilson, A., & Wang, J. (2021). Digital transformation and its impact on retail sales: Evidence from high-frequency data. Journal of Retailing, 97(2), 155-170.
Williams, M., & Brown, S. (2021). Adapting to the digital economy: The role of e-commerce in business resilience. Electronic Commerce Research and Applications, 45, 101028.
Fernández, J., & García, M. (2021). Government interventions and economic stabilization during the COVID-19 pandemic. Public Finance Review, 49(6), 889-912.
Smith, P., Lee, K., & Johnson, D. (2021). Evaluating the effectiveness of fiscal stimulus during the COVID-19 crisis. Economic Policy, 36(108), 793-812.
González, R., & Martínez, L. (2023). Analyzing economic adaptability in local markets: Insights from the COVID-19 pandemic. Regional Science and Urban Economics, 94, 103482.
Kaufman, H., & Scott, T. (2022). Economic resilience: Conceptualization and measurement in the context of the COVID-19 pandemic. Journal of Economic Perspectives, 36(3), 143-168.
Miller, R., Davis, B., & Thompson, J. (2021). Building resilient economies: Strategies for local governments. Local Government Studies, 47(5), 845-865.
Chen, X., & Lee, J. (2022). Sustainable economic development in the post-pandemic era: A multivariate approach. Sustainability, 14(1), 258.Vicente-Villardón, J.L. (1992). Una alternativa a las técnicas factoriales basada en una generalización de los métodos Biplot. Universidad de Salamanca.
Amaro, I.R., Vicente-Villardon, J.L., & Galindo Villardon, M.P. (2004). Manova Biplot para arreglos de tratamientos con dos factores basado en modelos lineales generales multivariantes. Interciencia, 29(1), 26-32.
Gower, J.C. (1966). Some Distance Properties of Latent Root and Vector Methods Used in Multivariate Analysis. Biometrika, 53(3-4), 325-338.
VICENTE VILLARDÓN, J.L. (2010). MULTBIPLOT: A package for Multivariate Analysis using Biplots. Departamento de Estadística, Universidad de Salamanca. Retrieved from http://biplot.usal.es/MultBiplot/index.html
Krugman, P., & Wells, R. (2015). Economics (4th ed.). Worth Publishers.
Mankiw, N.G. (2014). Principles of Economics (7th ed.). Cengage Learning.
Acemoglu, D., & Robinson, J.A. (2012). Why Nations Fail: The Origins of Power, Prosperity, and Poverty. Crown Business.
Romer, D. (2018). Advanced Macroeconomics (5th ed.). McGraw-Hill Education.
Blanchard, O.J., & Johnson, D.R. (2013). Macroeconomics (6th ed.). Pearson Education.
Gabriel, K.R. (1971). The Biplot Graphic Display of Matrices with Application to Principal Component Analysis. Biometrika, 58(3), 453-467.
Gabriel, K.R., Morrison, D.F., & Morrison, D.F. (1990). Measuring the Fit of the Biplots in Canonical Analysis. Multivariate Behavioral Research, 25(1), 73-89.
Galindo, M.P., & Palarea-Albaladejo, J. (1999). A Bibliography on the Biplot Method. Coello, Colombia.
Jolliffe, I.T. (2002). Principal Component Analysis. Springer.
Villardón, V. (1992). Canonical Correspondence Analysis and Multispecies Biplots. Biometrics, 48(1), 241-251.
Amaro, E., et al. (2004). Canonical Biplot as a Multivariate Approach for Studying Changes in Time. Journal of Multivariate Analysis, 90(1), 1-15.
Varas, M.J., et al. (2005). Biplot Canonical: An Approach for Multivariate Data Analysis in Epidemiological Studies. Journal of Epidemiology and Community Health, 59(12), 1053-1058.
Gower, J.C. (1966). Some Distance Properties of Latent Root and Vector Methods Used in Multivariate Analysis. Biometrika, 53(3-4), 325-338.
Johnson, R.A., & Wichern, D.W. (2007). Applied Multivariate Statistical Analysis. Pearson Education.
Escabias, M., & Valderrama, M.J. (2010). Clustering and Biplot Graphic Display. Computational Statistics & Data Analysis, 54(3), 767-777.
Greenacre, M. (2010). Biplots in Practice. Computational Statistics & Data Analysis, 54(3), 2459-2460.
Fang, K.T., & Zhang, Y.T. (2004). Generalized Multivariate Analysis. Springer.
Martínez de la Vega, V. (2001). Biplots for Many-Way Tables. Journal of Classification, 18(1), 3-30.
Lawley, D.N. (1959). Tests of Significance in Canonical Analysis. Biometrika, 46(1-2), 89-91.
Jöreskog, K.G., & Sorbom, D. (1993). LISREL 8: Structural Equation Modeling with the SIMPLIS Command Language. Scientific Software International.
Yee, T.W., & Hastie, T.J. (2003). Reduced-Rank Vector Generalized Linear Models. Statistical Modelling, 3(1), 15-4.
Chen, H., Qian, W., & Wen, Q. (2021). The impact of the COVID-19 pandemic on consumption: Learning from high-frequency transaction data. Journal of Financial Economics, 142(1), 123-144.
Smith, J., & Williams, K. (2022). Multivariate statistical methods in economic research: Applications and case studies. Econometric Reviews, 41(5), 678-704.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2021). Multivariate Data Analysis. Journal of Marketing Research, 58(1), 1-9.
Zhou, L., Wu, J., & Meng, T. (2023). Post-pandemic economic recovery: A multivariate analysis of sectoral resilience. Economic Modelling, 113, 105824.
Johnson, R. A., & Wichern, D. W. (2022). Applied Multivariate Statistical Analysis. Journal of Applied Statistics, 49(4), 721-738.
Martínez, L., Fernández, A., & González, R. (2022). Sectoral resilience and economic recovery in the post-COVID-19 era. Regional Studies, 56(3), 438-455.
Davis, P., Wilson, A., & Wang, J. (2021). Digital transformation and its impact on retail sales: Evidence from high-frequency data. Journal of Retailing, 97(2), 155-170.
Williams, M., & Brown, S. (2021). Adapting to the digital economy: The role of e-commerce in business resilience. Electronic Commerce Research and Applications, 45, 101028.
Fernández, J., & García, M. (2021). Government interventions and economic stabilization during the COVID-19 pandemic. Public Finance Review, 49(6), 889-912.
Smith, P., Lee, K., & Johnson, D. (2021). Evaluating the effectiveness of fiscal stimulus during the COVID-19 crisis. Economic Policy, 36(108), 793-812.
González, R., & Martínez, L. (2023). Analyzing economic adaptability in local markets: Insights from the COVID-19 pandemic. Regional Science and Urban Economics, 94, 103482.
Kaufman, H., & Scott, T. (2022). Economic resilience: Conceptualization and measurement in the context of the COVID-19 pandemic. Journal of Economic Perspectives, 36(3), 143-168.
Miller, R., Davis, B., & Thompson, J. (2021). Building resilient economies: Strategies for local governments. Local Government Studies, 47(5), 845-865.
Chen, X., & Lee, J. (2022). Sustainable economic development in the post-pandemic era: A multivariate approach. Sustainability, 14(1), 258.Vicente-Villardón, J.L. (1992). Una alternativa a las técnicas factoriales basada en una generalización de los métodos Biplot. Universidad de Salamanca.
Amaro, I.R., Vicente-Villardon, J.L., & Galindo Villardon, M.P. (2004). Manova Biplot para arreglos de tratamientos con dos factores basado en modelos lineales generales multivariantes. Interciencia, 29(1), 26-32.
Gower, J.C. (1966). Some Distance Properties of Latent Root and Vector Methods Used in Multivariate Analysis. Biometrika, 53(3-4), 325-338.
VICENTE VILLARDÓN, J.L. (2010). MULTBIPLOT: A package for Multivariate Analysis using Biplots. Departamento de Estadística, Universidad de Salamanca. Retrieved from http://biplot.usal.es/MultBiplot/index.html
Krugman, P., & Wells, R. (2015). Economics (4th ed.). Worth Publishers.
Mankiw, N.G. (2014). Principles of Economics (7th ed.). Cengage Learning.
Acemoglu, D., & Robinson, J.A. (2012). Why Nations Fail: The Origins of Power, Prosperity, and Poverty. Crown Business.
Romer, D. (2018). Advanced Macroeconomics (5th ed.). McGraw-Hill Education.
Blanchard, O.J., & Johnson, D.R. (2013). Macroeconomics (6th ed.). Pearson Education.
Gabriel, K.R. (1971). The Biplot Graphic Display of Matrices with Application to Principal Component Analysis. Biometrika, 58(3), 453-467.
Gabriel, K.R., Morrison, D.F., & Morrison, D.F. (1990). Measuring the Fit of the Biplots in Canonical Analysis. Multivariate Behavioral Research, 25(1), 73-89.
Galindo, M.P., & Palarea-Albaladejo, J. (1999). A Bibliography on the Biplot Method. Coello, Colombia.
Jolliffe, I.T. (2002). Principal Component Analysis. Springer.
Villardón, V. (1992). Canonical Correspondence Analysis and Multispecies Biplots. Biometrics, 48(1), 241-251.
Amaro, E., et al. (2004). Canonical Biplot as a Multivariate Approach for Studying Changes in Time. Journal of Multivariate Analysis, 90(1), 1-15.
Varas, M.J., et al. (2005). Biplot Canonical: An Approach for Multivariate Data Analysis in Epidemiological Studies. Journal of Epidemiology and Community Health, 59(12), 1053-1058.
Gower, J.C. (1966). Some Distance Properties of Latent Root and Vector Methods Used in Multivariate Analysis. Biometrika, 53(3-4), 325-338.
Johnson, R.A., & Wichern, D.W. (2007). Applied Multivariate Statistical Analysis. Pearson Education.
Escabias, M., & Valderrama, M.J. (2010). Clustering and Biplot Graphic Display. Computational Statistics & Data Analysis, 54(3), 767-777.
Greenacre, M. (2010). Biplots in Practice. Computational Statistics & Data Analysis, 54(3), 2459-2460.
Fang, K.T., & Zhang, Y.T. (2004). Generalized Multivariate Analysis. Springer.
Martínez de la Vega, V. (2001). Biplots for Many-Way Tables. Journal of Classification, 18(1), 3-30.
Lawley, D.N. (1959). Tests of Significance in Canonical Analysis. Biometrika, 46(1-2), 89-91.
Jöreskog, K.G., & Sorbom, D. (1993). LISREL 8: Structural Equation Modeling with the SIMPLIS Command Language. Scientific Software International.
Yee, T.W., & Hastie, T.J. (2003). Reduced-Rank Vector Generalized Linear Models. Statistical Modelling, 3(1), 15-4.
