Main Article Content

Abstract

This study compares the regression using the assumption of a normal distribution with a beta distribution on ratio/proportion data. The data used is the Gini ratio data as the dependent variable and the percentage of the poor, economic growth and unemployment as independent variables in 2021. The data used is sourced from the Central Statistics Agency. The criteria for selecting the best model are based on the smallest AIC and BIC criteria. The results obtained by the beta regression model are better than the model based on the normal distribution. This result is reflected by the probability value of the model suitability test and the error value which the smaller AIC and BIC reflect. The poverty variable has a significant effect on the Gini ratio. On the other hand, there is not enough evidence that the variables of economic growth and open unemployment affect the Gini ratio. From the results obtained, it is hoped that the government will be able to implement appropriate policies in overcoming inequality so that every level of society can feel welfare without exception.

Article Details

How to Cite
Sihombing, P. R. (2022). Comparison Of Normal-Based and Beta-Based Regression Models on Ratio/ Proportion Data. Jurnal Ekonomi Dan Statistik Indonesia, 2(1), 19-23. https://doi.org/10.11594/jesi.02.01.03

References

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Johnson, N., Kotz, S., & Balakrishnan, N. (1995). Continuous Univariate Distributions. Wiley.
Walpole, R. E. (2012). Probability & Statistics for Engineers & Scientists. Pearson.
Widarjono, A. (2007). Ekonometrika: Teori dan Aplikasi untuk Ekonomi dan Bisnis. Ekonosia Fakultas Ekonomi Universitas Islam Indonesia.
Agresti, A. (2002). Categorical Data Analysis. New York. Inc. John Wiley and Sons.
Akaike, H. (1974). A New Look at the Statistical Model Identification. IEEE Transactions on Automatic Control, 19(6), 716–723. https://doi.org/10.1109/TAC.1974.1100705
Apriesa, L. F., & Miyasto. (2013). Pengaruh Desentralisasi Fiskal terhadap Pertumbuhan Ekonomi Daerah dan Ketimpangan Pendapatan (Studi Kasus: Kabupaten/Kota di Jawa Tengah). Diponegoro Journal of Economics, 2(1), 1–12. https://ejournal3.undip.ac.id/index.php/jme/article/view/1916/1914
C J Swearingen, M. S. M. C. (2010). C J Swearingen, M S M Castro. Macro. SAS Global Forum 2011: Statistics and Data Analysis, 335–2011.
Farrah, N., & Yuliadi, I. (2020). Determinan Ketimpangan Distribusi Pendapatan di Indonesia. Proceedings The 1st UMYGrace 2020, 129–140.
Gideon Schwarz. (1978). Estimating The Dimension of a Model. The Annals of Statistics, 6(2), 461–464.
Gujarati, D. (2004). Basic Econometrics BY Gujarati (pp. 1–1002). McGraw-Hill Inc.
Hassan, S. A., Zaman, K., & Gul, S. (2015). The Relationship between Growth-Inequality-Poverty Triangle and Environmental Degradation: Unveiling the Reality. Arab Economic and Business Journal, 10(1), 57–71. https://doi.org/10.1016/j.aebj.2014.05.007
Hindun., Soejoto., A., & Hariyati. (2019). Pengaruh Pendidikan , Pengangguran , dan Kemiskinan terhadap Ketimpangan Pendapatan di Indonesia: Universitas, Pascasarjana Surabaya, Negeri Soejoto, Ady Universitas, Pascasarjana Surabaya, Negeri Universitas, Pascasarjana Surabaya, Negeri. 8(3), 250–265.
Johnson, N., Kotz, S., & Balakrishnan, N. (1995). Continuous Univariate Distributions. Wiley.
Walpole, R. E. (2012). Probability & Statistics for Engineers & Scientists. Pearson.
Widarjono, A. (2007). Ekonometrika: Teori dan Aplikasi untuk Ekonomi dan Bisnis. Ekonosia Fakultas Ekonomi Universitas Islam Indonesia