The week's pick
Reseach Article
A Comprehensive Study on the Factors Impacting the GDP (per capita) of Major Economies around the Globe using Regression Analysis
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 175 - Number 10 |
| Year of Publication: 2020 |
| Authors: Nimisha Bhide, Saurabh Khanolkar |
10.5120/ijca2020920560
|
Nimisha Bhide, Saurabh Khanolkar . A Comprehensive Study on the Factors Impacting the GDP (per capita) of Major Economies around the Globe using Regression Analysis. International Journal of Computer Applications. 175, 10 ( Aug 2020), 26-30. DOI=10.5120/ijca2020920560
Abstract
Financial Architecture aims at sustainability of an Economy. This is done by ensuring a consistent growth rate. GDP is a strong indicator of the growth of an economy. A Higher GDP of an economy reflects a robust growth. This leads to the definition of GDP (per capita). This study focuses on the GDP (per capita) as an indicator of a nation’s prosperity. The ratio of the GDP of an economy to its population is termed as the GDP (per capita). This study considers GDP (per capita) as a function of 17 factors. Further on, out of these 17 factors, 5 of the most statistically significant factors are identified using the Backward Elimination Algorithm. Thus, a statistically significant regression model is designed and the impact of each of the 5 factors on the GDP (per capita) is gauged. It was found that the combination of the aforementioned 5 statistically significant variables could explain 83% of the variance in the GDP (per capita) of the economies. The F statistic increased from 51.13(before applying Backward Elimination Algorithm); to 168.6 (after the application of the Algorithm) and hence, signifying the increase in the overall significance of the model. The authors firmly believe that that this study will form a foundation to the higher level policy making in the future.
References
- Divya K. and Devi V., 2014. A Study on Predictors of GDP: Early Signals. Procedia Economics and Finance, 11, pp.375-382.
- Farrar, D. E., & Glauber, R. R. (1967). Multicollinearity in regression analysis: the problem revisited. The Review of Economic and Statistics, 92-107.
- Breusch, T. S., & Pagan, A. R. (1979). A simple test for heteroscedasticity and random coefficient variation. Econometrica: Journal of the Econometric Society, 1287-1294
- Wang, H. and Rhee, W., 1995. An algorithm for estimating the parameters in multiple linear regression model with linear constraints. Computers & Industrial Engineering, 28(4), pp.813-821.
- Turóczy, Z. and Marian, L., 2012. Multiple Regression Analysis of Performance Indicators in the Ceramic Industry. Procedia Economics and Finance, 3, pp.509-514.
- TY - JOUR, Maity, Bipasha and Chatterjee, B.,2012/01/01 Forecasting GDP Growth Rates of India.: An Empirical Study.Int. J. Econom. Manage. Sci.,1
- FENG, R., 2019. Effect of Physical Education on Employment Ability of University Students Based on Multiple Linear Regression Analysis. DEStech Transactions on Economics, Business and Management, (emba).
- Karamazova, E., Jusufi Zenku, T. and Trifunov, Z., 2017. Analysing and Comparing the Final Grade in Mathematics by Linear Regression Using Excel and SPSS. International Journal of Mathematics Trends and Technology, 52(5), pp.334-344.
- Kumar, S. and Muhuri, P., 2019. A novel GDP prediction technique based on transfer learning using CO2 emission dataset. Applied Energy, 253, p.113476.
- Saqib, N., 2013. The Effect of Exchange Rate Fluctuation on Trade Balance: Empirical Evidence from Saudi Arab Economy. SSRN Electronic Journal.
- Lehmann, R. and Wohlrabe, K., 2015. Forecasting GDP at the Regional Level with Many Predictors. German Economic Review, 16(2), pp.226-254.
Index Terms
Keywords