Volume 10, Issue 35 (3-2019)
jemr 2019, 10(35): 145-166 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Majed V, Mirshojaeian Hosseini H, Riazi ِdoust S. Using Clustering and Factor Analysis in Cross Section Analysis Based on Economic-Environment Factors. jemr 2019; 10 (35) :145-166
URL: http://jemr.khu.ac.ir/article-1-1630-en.html
URL: http://jemr.khu.ac.ir/article-1-1630-en.html
Using Clustering and Factor Analysis in Cross Section Analysis Based on Economic-Environment Factors
1- University of Tehran , vahid.majed@gmail.com
2- Economic Affairs Research Institute
3- University of Tehran
2- Economic Affairs Research Institute
3- University of Tehran
Abstract: (4686 Views)
Homogeneity of groups in studies those use cross section and multi-level data is important. Most studies in economics especially panel data analysis need some kinds of homogeneity to ensure validity of results. This paper represents the methods known as clustering and homogenization of groups in cross section studies based on enviro-economics components. For this, a sample of 92 countries which produce the most greenhouse gases including CO2, clustered based on 18 criteria. Those criteria reduced to five primary components using factor analysis. Clustering of countries done by HCPC (Hierarchical Clustering on Principal Component) method. All 92 countries were clustered in 7 different groups. For each group properties of countries indicates the homogeneity of each cluster. In cross section analysis with many sections, especially analysis based on panel data, clustering, increases assurance of expected homogeneity and validity of result.
Keywords: Dentist Cluster Analysis, Factor Analysis, Cross Section Studies, Enviro-Economic Components
Type of Study: Applicable |
Subject:
انرژی، منابع و محیط زیست
Received: 2018/06/4 | Accepted: 2019/03/9 | Published: 2019/06/10
Received: 2018/06/4 | Accepted: 2019/03/9 | Published: 2019/06/10
References
1. Bakirtas, I., Bayrak, S., & Cetin, A. (2014). Economic growth and carbon emission: A dynamic panel data analysis. European Journal of Sustainable Development, 3(4), 91-102. [DOI:10.14207/ejsd.2014.v3n4p91]
2. Belke, A., Dreger, Ch., Haan, F., (2010), Energy Consumption and Economic Growth -New Insights into the Cointegration Relationship, Ruhr Economic Papers, (190). [DOI:10.2139/ssrn.1635765]
3. Dinda. S., & Coondo. D. (2006), Income and emission: a panel data-based cointagration analysis. Ecological Economics, (57), 167-181. [DOI:10.1016/j.ecolecon.2005.03.028]
4. Du, K. (2018). Econometric convergence test and club clustering using Stata. The Stata Journal, 17(4), 882-900. [DOI:10.1177/1536867X1801700407]
5. ESSO, J.L. (2010), The Energy Consumption-Growth Nexus in Seven Sub-Saharan African Countries,, Issue 2,(30), 1191-1209.
6. Huang, B., Hwang, M.J., Yang, C.W., (2008), Causal relationship between energy consumption and GDP growth revisited: A dynamic panel data approach, Ecological Economics, (67), 41-54. [DOI:10.1016/j.ecolecon.2007.11.006]
7. Husson, F., Josse, J., & Pagѐs. J. (2010). Principal component methods-hierarchical clustering-partitional clustering: why would we need to choose for visualizing data?, Agrocampus, 1.
8. KALANTARI, K. 2003. Data Processing and Analysis in Socio-Economic Research. Sharif Publication, Tehran.[Persian]
9. Kapetanios, G. (2005). Cluster Analysis of Panel Datasets using Non-Standard Optimisation of Information Criteria. Queen Mary, University of London, (535).
10. Lee, C. & Chang, C. (2007), Energy consumption and economic growth in Asian countries: A more comprehensive analysis using panel data, Resource and Energy Economics. [DOI:10.1016/j.reseneeco.2007.03.003]
11. Liao, N., & He, Y. (2018). Exploring the effects of influencing factors on energy efficiency in industrial sector using cluster analysis and panel regression model. Energy, 158, 782-795. [DOI:10.1016/j.energy.2018.06.049]
12. Mahdavi G, Majed V.(2011). The impact of Socio-Economic and Psychological Factors on Life Insurance Demand in Iran. JEMR. 2 (5) :21-46. .[Persian]
13. McNeish, D., & Kelley, K. (2018). Fixed effects models versus mixed effects models for clustered data: Reviewing the approaches, disentangling the differences, and making recommendations. Psychological methods. [DOI:10.1037/met0000182]
14. Pao. H. T., & Tsai. C. M. (2010), "CO2 emissions, energy consumption and economic growth in BRIC countries". Energy Policy, (38), 7850-7860. [DOI:10.1016/j.enpol.2010.08.045]
15. Pourkazemi, M., & Ebrahimi, I. (2008). Evaluation of environmental Kuznets curve in the Middle East. Journal of Economic Research, 34, 57-71.[Persian]
16. Rafat M. (2019). The Application of Complex Networks Analysis to Assess Iran's Trade and It's Most Important Trading Partners in Asia. JEMR. 9 (34) :107-137. .[Persian]
17. Squalli, J. (2006), Electricity consumption and economic growth: Bounds and causality analyses of OPEC members, Energy economics. [DOI:10.1016/j.eneco.2006.10.001]
18. Stolyarova, E. (2013). "Carbon Dioxide Emissions, economic growth andenergy mix: empirical evidence from 93 countries". Climate Economics Chair.
19. Wolde-Rufael, Y. (2006), Electricity consumption and economic growth: a time series experience for 17 African countries, Energy Policy, (34), 1106-1114. [DOI:10.1016/j.enpol.2004.10.008]
20. Wooldrige, M. (2006). CLUSTER-SAMPLE METHODS IN APPLIED ECONOMETRICS: AN EXTENDED ANALYSIS. Michigan State University. (517) 353-5972.
21. www.bp.com. Statistical Review of World Energy (2016)
22. www.eia.gov. (2016)
23. www.worldbank.org. World Development Indicator (2016)
Send email to the article author
| Rights and permissions | |
 | This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
