MULTINOMIAL LOGISTIC REGRESSION MODEL USING MAXIMUM LIKELIHOOD APPROACH AND BAYES METHOD ON INDONESIA'S ECONOMIC GROWTH PRE TO POST COVID-19 PANDEMIC
Abstract
Economic growth in Indonesia has become a major concern in the global context, especially before and after the Covid-19 pandemic. Key sectors such as tourism, manufacturing, trade and transportation have been seriously affected by restrictions on travel and economic activity imposed to control the spread of the virus. Therefore, it is considered necessary to carry out modeling to describe existing conditions. In this research, two approaches were used, namely the Maximum Likelihood approach and the Bayes approach. The use of methods in general as research material for researchers to study these two methods further. So far the algorithm used for the Bayes concept method is Markov Chain Monte Carlo with Hasting's Metropolis method. The parameter estimation results obtained from both methods are considered quite identical. However, it is necessary to pay attention to the iteration procedure that will be carried out. The selection of factors used in the iteration process is very determining in obtaining estimated parameter values. Furthermore, the results obtained so far do not contain any fundamental differences regarding economic growth in Indonesia. In general, Indonesia can be said to be stable in terms of economic growth.
Downloads
References
W. Priana and D. K. Sawitri, “Indonesia’s Economic Growth from 2019 -2021: An Economic Overview Before and During COVID 19 Pandemic,” in Nusantara Science and Technology Proceedings., 2021.
Badan Pusat Statistik Indonesia, “‘Statistik Indonesia 2020,’” 2021.
Bank Indonesia, “Indonesia’s Economic Review,” 2021.
International Monetary Fund, “Indonesia: Staff Concluding Statement of the 2022 Article IV Mission,” 2022.
World Trade Organization, “Trade and the COVID-19 Pandemic,” 2022.
A. Agresti, Categorical Data Analysis. John Wiley & Sons., 2002.
J. H. Stock and M. W. Watson, Introduction to Econometrics. London: Pearson, 2019.
D. W. Hosmer, S. Lemeshow, and R. X. Sturdivant, Applied Logistic Regression. John Wiley & Sons., 2013.
A. Gelman, J. B. Carlin, H. S. Stern, D. B. Dunson, A. Vehtari, and D. B. Rubin, Bayesian Data Analysis (3rd ed.). Florida: CRC Press, 2013.
P. . Sihombing, S. R. Rohimah, and A. Kurnia, “Comparison Between The Maximum Likelihood and The Bayesian Estimation Methods for Logistic Regression Model (case study: risk of low birth weight in Indonesia),” in Journal of Physics: Conference Series, 2106, 2021.
Y. Altinisik, “A comparative study on the performance of frequentist and Bayesian estimation methods under separation in logistic regression,” in Communications Faculty Of Science University of Ankara Series A1Mathematics and Statistics, 2020.
A. Agresti, Foundations of Linear and Generalized Linear Models. New Jersey: John Wiley & Sons, Inc, 2015.
S. M. A. Aljeddani and M. A. Mohammed, “Parameter estimation of a model using maximum likelihood function and Bayesian analysis through moment of order statistics,” Alexandria Eng. J., vol. 75, pp. 221–232, 2023, doi: 10.1016/j.aej.2023.05.079.
J. Neyman and E. S. Pearson, “On the Use and Interpretation of Certain Test Criteria for Purposes of Statistical Inference,” Biometrika, vol. 20A, no. 1/2, pp. 175–240, 1928.
A. Wald, “Tests of Statistical Hypotheses Concerning Several Parameters When The Number of Observations Is Large,” Trans. Am. Math. Soc., vol. 54, no. 3, pp. 426–482, 1943.
J. M. Wooldridge, Econometric Analysis of Cross Section and Panel Data. MIT Press, 2010.
N. Metropolis, A. W. Rosenbluth, M. N. Rosenbluth, A. H. Teller, and E. Teller, “Equation of State Calculations by Fast Computing Machines,” J. Chem. Phys., vol. 21, no. 6, pp. 1087–1092, 1953.
W. K. Hasting, “Monte Carlo Sampling Methods Using Markov Chains and Their Applications,” Biometrika, vol. 57, no. 1, pp. 97–109, 1970.
J. Lenhard, “A transformation of Bayesian statistics:Computation, prediction, and rationality,” Stud. Hist. Philos. Sci., vol. 92, no. February 2021, pp. 144–151, 2022, doi: 10.1016/j.shpsa.2022.01.017.
J. K. Kruschke, Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan. Academic Press, 2014.
V. Roy, “Convergence diagnostics for markov chain monte carlo,” Annu. Rev. Stat. Its Appl., vol. 7, pp. 387–412, 2020, doi: 10.1146/annurev-statistics-031219-041300.
S. Roy, M. Chain, M. Carlo, A. Rev, and S. Appl, “Convergence Diagnostics for,” pp. 1–26, 2020.
Copyright (c) 2025 Arie Purwanto, Muhammad Aziz Suprayogi, Erwan Setiawan, Joao Ferreira Rendes Bean Loly, Gusti Arviana Rahman, Anang Kurnia
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this Journal agree to the following terms:
- Author retain copyright and grant the journal right of first publication with the work simultaneously licensed under a creative commons attribution license that allow others to share the work within an acknowledgement of the work’s authorship and initial publication of this journal.
- Authors are able to enter into separate, additional contractual arrangement for the non-exclusive distribution of the journal’s published version of the work (e.g. acknowledgement of its initial publication in this journal).
- Authors are permitted and encouraged to post their work online (e.g. in institutional repositories or on their websites) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published works.
1.gif)
