.svg){kind=logo}
BAYESIAN MULTIPLE LINEAR REGRESSION MODEL FOR GROSS DOMESTIC PRODUCT IN BHUTAN
Keywords:
economic, Bayesian, gross domestic product, Bhutan, integrated nested Laplace approximation, regression, deviance information criterionDOI:
https://doi.org/10.17654/0972361723033Abstract
The Gross Domestic Product (GDP), often referred to as the economy’s heartbeat, depends on a number of variables, including export-import balance, inflation, and unemployment rates. For the estimation process to be improved and strengthened, the statistical analysis of GDP requires new concepts to explain GDP through its covariates. Heatmaps are used to illustrate descriptive statistics for the World Bank’s considered data set for GDP and associated variables. Ordinary Least Square (OLS) and step-wise regression are used to identify and assess the relevance of a potential collection of covariates. In order to explain Bhutan’s GDP, we suggest an alternative statistical approach called Bayesian Inference using Integrated Nested Laplace Approximation (INLA), which closes the accuracy gap between estimates and frequentist OLS regression. Deviance Information Criterion is used to evaluate the impact of altering previous parameters (DIC). Results from frequentist versus Bayesian modelling are compared using a variety of metrics, including Mean Square Error (MSE), Mean Absolute Deviation (MAD), and Mean Absolute Percent Error (MAPE). In comparison to the traditional OLS method, the Bayesian estimation methodology is a more effective technique for parametric estimation.
Received: February 20, 2023
Revised: March 8, 2023
Accepted: April 5, 2023
References
M. Akter, The impact of export and import on economic growth in Bangladesh, World Vision 9(1) (2015), 66-81.
W. M. Alshuwaiee, N. N. AlShammari and N. W. Al-Mubarak, Is unemployment caused by a socioeconomic phenomenon in the case of Kuwait?, Global Business and Economics Review 23(4) (2020), 412-431.
A. Bashir and A. Amir, Relationship between government expenditure on education and GDP per capita in Pakistan: An ARDL Approach to Co-integration, Advances and Applications in Statistics 55(1) (2019), 77-103.
L. E. Eberly, Multiple linear regression, Topics in Biostatistics (2007), 165-187.
O. Eric, G. Popovic and S. Popovic, Foreign trade liberalisation and economic growth: The case of the Republic of Srpska, Economics 7(2) (2019), 99-108.
A. Gelman, J. Hwang and A. Vehtari, Understanding predictive information criteria for Bayesian models, Statistics and Computing 24(6) (2014), 997-1016.
A. Gelman, D. Lee and J. Guo, Stan: A probabilistic programming language for Bayesian inference and optimization, Journal of Educational and Behavioral Statistics 40(5) (2015), 530-543.
H. Z. Javed, I. Qaiser, A. Mushtaq, Saif-Ullah and A. Iqbal, Effects of international trade on economic growth: The case study of Pakistan, International Journal of Academic Research in Progressive Education and Development 1(2) (2012), 103-113.
P. Laplace, Memoire sur la Probabilite des Causes par les Evenements, Academie Royale des Sciences 6 (1774), 621-656.
English translation by S. M. Stigler in 1986 as “Memoir on the Probability of the Causes of Events” in Statistical Science, 1(3), 359-378.
M. Muhammed Al-Kassab, The effect of some macroeconomic variables on stock market movement in Iraq, Advances and Applications in Statistics 73 (2022), 1-16.
National Statistics Bureau of Bhutan (NSB), Statistical Yearbook of Bhutan, Thimphu: Royal Government of Bhutan, 2020.
J. Nidup, S. Feeny and A. de Silva, Improving well-being in Bhutan: A Pursuit of Happiness, Social Indicators Research 140(1) (2018), 79-100. doi: 10.1007/s11205-017-1775-1
M. Nurmakhanova, Oil and growth challenge in Kazakhstan, International Journal of Economics and Business Research 20(1) (2020), 100-116.
A. T. Othman and M. M. Al-Kassab, Using ridge regression to estimate some variables affecting the Iraqi stock exchange index, Advances and Applications in Statistics 69(2) (2021), 191-202.
S. D. Permai and H. Tanty, Linear regression model using Bayesian approach for energy performance of residential building, Procedia Computer Science 135 (2018), 671-677.
M. Plummer, Rjags: Bayesian graphical models using MCMC, R Software Package for Graphical Models, 2016. https://cran.r-project.org/web/packages/rjags/index.html
C. P. Robert and G. Casella, Monte Carlo Statistical Methods, New York, Springer-Verlag, 1999.
H. Rue, S. Martino and N. Chopin, Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations, Journal of the Royal Statistical Society: Series b (Statistical Methodology) 71(2) (2009), 319-392.
D. I. Siregar and R. R. Alhempi, Analysis of the regression and correlation of gross domestic product (GDP) exports and revenue (GDP) of Indonesia against Rupiah Exchange Rate (IDR-USD), Journal of Global Economics 6(4) (2018), 10.4172/2375-4389.1000320
D. J. Spiegelhalter, A. Thomas, N. G. Best and W. R. Gilks, BUGS: Bayesian Inference using Gibbs Sampling, Software Package for Performing Bayesian Inference using Markov Chain Monte Carlo, 1995.
D. J. Spiegelhalter, N. G. Best, B. P. Carlin and A. Van Der Linde, Bayesian measures of model complexity and fit, Journal of the Royal Statistical Society: Series b (Statistical Methodology), 64(4) (2002), 583-639.
S. Stanic and Z. V. Racic, Analysis of macroeconomic factors effect to gross domestic product of Bosnia and Herzegovina using the multiple linear regression model, Economics-Innovative and Economic Research 7(2) (2019), 91-97.
Takahiro Akita and Dorji Lethro, Pro-poorness of rural economic growth and the roles of education in Bhutan, 2007-2017, Journal of the Asia Pacific Economy (2022), 1-27.
G. Tenzin, K. Otsuka and K. Natsuda, Can social capital reduce poverty? A study of rural households in eastern Bhutan, Asian Economic Journal 29(3) (2015), 243 264. doi: 10.1111/asej.12057
L. Tierney and J. B. Kadane, Accurate approximations for posterior moments and marginal densities, Journal of the American Statistical Association 81(393) (1986), 82-86.
J. D. Urrutia and R. L. Tampis, Regression analysis of the economic factors of the gross domestic product in the Philippines, Journal of Fundamental and Applied Sciences 9(7S) (2017), 190-201.
World Bank, World Development Indicators, World Bank, Washington, DC., 2022.
Downloads
Published
Issue
Section
License
Copyright (c) 2023 Pushpa Publishing House, Prayagraj, India
This work is licensed under a Creative Commons Attribution 4.0 International License.
____________________________
Attribution: Credit Pushpa Publishing House as the original publisher, including title and author(s) if applicable.
No Derivatives: Modifying or creating derivative works not allowed without written permission.
Contact Pushpa Publishing House for more info or permissions.
Journal Impact Factor: 
