Bayesian Multiple Linear Regression Model for GDP in Nepal

Ranjita Pandey; Dipendra Bahadur Chand; Himanshu Tolani

doi:doi:10.11648/j.ijsd.20230901.12

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Bayesian Multiple Linear Regression Model for GDP in Nepal

Ranjita Pandey, Dipendra Bahadur Chand, Himanshu Tolani

Published in International Journal of Statistical Distributions and Applications (Volume 9, Issue 1)

Received: 13 January 2023 Accepted: 6 February 2023 Published: 27 February 2023

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Abstract

Gross Domestic Product (GDP) known as the pulse of economy for any country depends on multiple factors like export-import, inflation rate and unemployment rate etc. Statistical assessment of GDP demands fresh concepts to explain GDP through its covariates in order to improve and strengthen estimation process. Descriptive analysis for the considered data set from world bank for GDP and covariates is presented through Heatmaps. Identification and relevance of possible set of covariates is done by Ordinary Least Square (OLS) regression followed by Step-wise regression. We propose an alternative statistical algorithm implemented as Bayesian Inference through Integrated Nested Laplace Approximation (INLA) which bridges the gap of accuracy in estimates as opposed to frequentist OLS regression for explaining GDP of country Nepal. Effect of changing prior parameters is assessed through Deviance Information Criterion (DIC). Different scenarios for prior distribution for regression parameters were analyzed to identify most suitable choice of parameter for normal distribution. The comparison of Bayesian and frequentist modelling results is done using several criteria such as Mean Square Error (MSE), Mean Absolute Deviation (MAD) and Mean Absolute Percent Error (MAPE). Bayesian estimation approach is a more efficient method for parametric estimation as compared to OLS classical method. GDP of Nepal was found to have strongest relationship with unemployment rate of Nepal as evident from both classical and Bayesian model.

Published in	International Journal of Statistical Distributions and Applications (Volume 9, Issue 1)
DOI	10.11648/j.ijsd.20230901.12
Page(s)	9-23
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Bayesian, GDP, Nepal, INLA, Regression, DIC

References

[1]	Akter, M. (2015), The impact of export and import on economic growth in Bangladesh. World Vision, 9 (1), 66-81.
[2]	Alshuwaiee, W. M., AlShammari, N. N., & Al-Mubarak, N. W. (2020). Is unemployment caused by a socioeconomic phenomenon in the case of Kuwait?. Global Business and Economics Review, 23 (4), 412-431.
[3]	Central Bureau of Statistics (2022). CBS Report. Government of Nepal, Kathmandu, Nepal.
[4]	Eberly, L. E. (2007). Multiple linear regression. Topics in Biostatistics, 165-187.
[5]	Eric, O., Popovic, G., Popović, S. (2019), Foreign trade liberalisation and economic growth: The case of the Republic of Srpska. Economics, 7 (2), 99-108.
[6]	Gelman, A., Hwang, J., & Vehtari, A. (2014). Understanding predictive information criteria for Bayesian models. Statistics and computing, 24 (6), 997-1016.
[7]	Gelman, A., Lee, D., & Guo, J. (2015). Stan: A probabilistic programming language for Bayesian inference and optimization. Journal of Educational and Behavioral Statistics, 40 (5), 530-543.
[8]	Javed, H. Z., Qaiser, I., Mushtaq, A., Saif-Ullah, Iqbal, A. (2012), Effects of international trade on economic growth: The case study of Pakistan. International Journal of Academic Research in Progressive Education and Development, 1 (2), 103-113.
[9]	Laplace P (1774). “Memoire sur la Probabilite des Causes par les Evenements.” Academie Royale des Sciences, 6, 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.
[10]	Ministry of Culture, Tourism and Civil Aviation (2020). Nepal Tourism Statistics: Ministry of Culture, Tourism and Civil Aviation, GON.
[11]	Nurmakhanova, M. (2020). Oil and growth challenge in Kazakhstan. International Journal of Economics and Business Research, 20 (1), 100-116.
[12]	Padder A. H, Mathavan B. 2021. The Relationship between Unemployment and Economic Growth in India: Granger Causality Approach. Natural Volatiles and Essential Oils, 8 (04): 1265–1271.
[13]	Permai, S. D., & Tanty, H. (2018). Linear regression model using bayesian approach for energy performance of residential building. Procedia Computer Science, 135, 671-677.
[14]	Plummer M. (2016). Rjags: Bayesian graphical models using MCMC. R Software Package for Graphical Models.
[15]	Robert CP, Casella G. (1999). Monte Carlo Statistical Methods. New York: Springer-Verlag.
[16]	Rue, H., Martino, S., & Chopin, N. (2009). 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), 319-392.
[17]	Sharma S. (2019). The Rhetoric of Unemployment, Racism and Sexism in Nepal. International Journal of Recent Academic Research, 1 (07): 336–339.
[18]	Singh, D. A. and Khanal, R. K. (2010). Pattern of Indo-Nepal Trade After 1990. Socioeconomic Voice.
[19]	Siregar, D. I., & Alhempi, R. R. (2018). 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), 10-4172.
[20]	Spiegelhalter DJ, Thomas A, Best NG, Gilks WR. (1995). BUGS: Bayesian inference using Gibbs sampling. Software Package for Performing Bayesian Inference Using Markov Chain Monte Carlo.
[21]	Spiegelhalter, D. J., Best, N. G., Carlin, B. P., & Van Der Linde, A. (2002). Bayesian measures of model complexity and fit. Journal of the royal statistical society: Series b (statistical methodology), 64 (4), 583-639.
[22]	Stani, S., & Rai Ž. V. (2019). 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), 91-97.
[23]	Thaba T. K, Belete A, Hlongwane J. J, Ledwaba L. J. (2020). Econometric estimation of the relationship between the unemployment rate and economic growth in Limpopo Province, South Africa. Journal of Agribusiness and Rural Development, 58 (4).
[24]	Tierney, L., & Kadane, J. B. (1986). Accurate approximations for posterior moments and marginal densities. Journal of the American statistical association, 81 (393), 82-86.
[25]	Urrutia, J. D., & Tampis, R. L. (2017). Regression analysis of the economic factors of the gross domestic product in the Philippines. Journal of Fundamental and Applied Sciences, 9 (7S), 190-201.
[26]	World Bank (2021). World Development Indicators Database. Washington D. C.

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APA Style

Ranjita Pandey, Dipendra Bahadur Chand, Himanshu Tolani. (2023). Bayesian Multiple Linear Regression Model for GDP in Nepal. International Journal of Statistical Distributions and Applications, 9(1), 9-23. https://doi.org/10.11648/j.ijsd.20230901.12

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ACS Style

Ranjita Pandey; Dipendra Bahadur Chand; Himanshu Tolani. Bayesian Multiple Linear Regression Model for GDP in Nepal. Int. J. Stat. Distrib. Appl. 2023, 9(1), 9-23. doi: 10.11648/j.ijsd.20230901.12

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AMA Style

Ranjita Pandey, Dipendra Bahadur Chand, Himanshu Tolani. Bayesian Multiple Linear Regression Model for GDP in Nepal. Int J Stat Distrib Appl. 2023;9(1):9-23. doi: 10.11648/j.ijsd.20230901.12

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@article{10.11648/j.ijsd.20230901.12,
  author = {Ranjita Pandey and Dipendra Bahadur Chand and Himanshu Tolani},
  title = {Bayesian Multiple Linear Regression Model for GDP in Nepal},
  journal = {International Journal of Statistical Distributions and Applications},
  volume = {9},
  number = {1},
  pages = {9-23},
  doi = {10.11648/j.ijsd.20230901.12},
  url = {https://doi.org/10.11648/j.ijsd.20230901.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijsd.20230901.12},
  abstract = {Gross Domestic Product (GDP) known as the pulse of economy for any country depends on multiple factors like export-import, inflation rate and unemployment rate etc. Statistical assessment of GDP demands fresh concepts to explain GDP through its covariates in order to improve and strengthen estimation process. Descriptive analysis for the considered data set from world bank for GDP and covariates is presented through Heatmaps. Identification and relevance of possible set of covariates is done by Ordinary Least Square (OLS) regression followed by Step-wise regression. We propose an alternative statistical algorithm implemented as Bayesian Inference through Integrated Nested Laplace Approximation (INLA) which bridges the gap of accuracy in estimates as opposed to frequentist OLS regression for explaining GDP of country Nepal. Effect of changing prior parameters is assessed through Deviance Information Criterion (DIC). Different scenarios for prior distribution for regression parameters were analyzed to identify most suitable choice of parameter for normal distribution. The comparison of Bayesian and frequentist modelling results is done using several criteria such as Mean Square Error (MSE), Mean Absolute Deviation (MAD) and Mean Absolute Percent Error (MAPE). Bayesian estimation approach is a more efficient method for parametric estimation as compared to OLS classical method. GDP of Nepal was found to have strongest relationship with unemployment rate of Nepal as evident from both classical and Bayesian model.},
 year = {2023}
}

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TY  - JOUR
T1  - Bayesian Multiple Linear Regression Model for GDP in Nepal
AU  - Ranjita Pandey
AU  - Dipendra Bahadur Chand
AU  - Himanshu Tolani
Y1  - 2023/02/27
PY  - 2023
N1  - https://doi.org/10.11648/j.ijsd.20230901.12
DO  - 10.11648/j.ijsd.20230901.12
T2  - International Journal of Statistical Distributions and Applications
JF  - International Journal of Statistical Distributions and Applications
JO  - International Journal of Statistical Distributions and Applications
SP  - 9
EP  - 23
PB  - Science Publishing Group
SN  - 2472-3509
UR  - https://doi.org/10.11648/j.ijsd.20230901.12
AB  - Gross Domestic Product (GDP) known as the pulse of economy for any country depends on multiple factors like export-import, inflation rate and unemployment rate etc. Statistical assessment of GDP demands fresh concepts to explain GDP through its covariates in order to improve and strengthen estimation process. Descriptive analysis for the considered data set from world bank for GDP and covariates is presented through Heatmaps. Identification and relevance of possible set of covariates is done by Ordinary Least Square (OLS) regression followed by Step-wise regression. We propose an alternative statistical algorithm implemented as Bayesian Inference through Integrated Nested Laplace Approximation (INLA) which bridges the gap of accuracy in estimates as opposed to frequentist OLS regression for explaining GDP of country Nepal. Effect of changing prior parameters is assessed through Deviance Information Criterion (DIC). Different scenarios for prior distribution for regression parameters were analyzed to identify most suitable choice of parameter for normal distribution. The comparison of Bayesian and frequentist modelling results is done using several criteria such as Mean Square Error (MSE), Mean Absolute Deviation (MAD) and Mean Absolute Percent Error (MAPE). Bayesian estimation approach is a more efficient method for parametric estimation as compared to OLS classical method. GDP of Nepal was found to have strongest relationship with unemployment rate of Nepal as evident from both classical and Bayesian model.
VL  - 9
IS  - 1
ER  -

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Author Information

Ranjita Pandey

Department of Statistics, University of Delhi, New Delhi, India
Dipendra Bahadur Chand

Department of Statistics, University of Delhi, New Delhi, India
Himanshu Tolani

International Institute of Health Management and Research, New Delhi, India

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Plain Text BibTeX RIS

APA Style

Ranjita Pandey, Dipendra Bahadur Chand, Himanshu Tolani. (2023). Bayesian Multiple Linear Regression Model for GDP in Nepal. International Journal of Statistical Distributions and Applications, 9(1), 9-23. https://doi.org/10.11648/j.ijsd.20230901.12

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ACS Style

Ranjita Pandey; Dipendra Bahadur Chand; Himanshu Tolani. Bayesian Multiple Linear Regression Model for GDP in Nepal. Int. J. Stat. Distrib. Appl. 2023, 9(1), 9-23. doi: 10.11648/j.ijsd.20230901.12

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AMA Style

Ranjita Pandey, Dipendra Bahadur Chand, Himanshu Tolani. Bayesian Multiple Linear Regression Model for GDP in Nepal. Int J Stat Distrib Appl. 2023;9(1):9-23. doi: 10.11648/j.ijsd.20230901.12

Copy | Download

@article{10.11648/j.ijsd.20230901.12,
  author = {Ranjita Pandey and Dipendra Bahadur Chand and Himanshu Tolani},
  title = {Bayesian Multiple Linear Regression Model for GDP in Nepal},
  journal = {International Journal of Statistical Distributions and Applications},
  volume = {9},
  number = {1},
  pages = {9-23},
  doi = {10.11648/j.ijsd.20230901.12},
  url = {https://doi.org/10.11648/j.ijsd.20230901.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijsd.20230901.12},
  abstract = {Gross Domestic Product (GDP) known as the pulse of economy for any country depends on multiple factors like export-import, inflation rate and unemployment rate etc. Statistical assessment of GDP demands fresh concepts to explain GDP through its covariates in order to improve and strengthen estimation process. Descriptive analysis for the considered data set from world bank for GDP and covariates is presented through Heatmaps. Identification and relevance of possible set of covariates is done by Ordinary Least Square (OLS) regression followed by Step-wise regression. We propose an alternative statistical algorithm implemented as Bayesian Inference through Integrated Nested Laplace Approximation (INLA) which bridges the gap of accuracy in estimates as opposed to frequentist OLS regression for explaining GDP of country Nepal. Effect of changing prior parameters is assessed through Deviance Information Criterion (DIC). Different scenarios for prior distribution for regression parameters were analyzed to identify most suitable choice of parameter for normal distribution. The comparison of Bayesian and frequentist modelling results is done using several criteria such as Mean Square Error (MSE), Mean Absolute Deviation (MAD) and Mean Absolute Percent Error (MAPE). Bayesian estimation approach is a more efficient method for parametric estimation as compared to OLS classical method. GDP of Nepal was found to have strongest relationship with unemployment rate of Nepal as evident from both classical and Bayesian model.},
 year = {2023}
}

Copy | Download

TY  - JOUR
T1  - Bayesian Multiple Linear Regression Model for GDP in Nepal
AU  - Ranjita Pandey
AU  - Dipendra Bahadur Chand
AU  - Himanshu Tolani
Y1  - 2023/02/27
PY  - 2023
N1  - https://doi.org/10.11648/j.ijsd.20230901.12
DO  - 10.11648/j.ijsd.20230901.12
T2  - International Journal of Statistical Distributions and Applications
JF  - International Journal of Statistical Distributions and Applications
JO  - International Journal of Statistical Distributions and Applications
SP  - 9
EP  - 23
PB  - Science Publishing Group
SN  - 2472-3509
UR  - https://doi.org/10.11648/j.ijsd.20230901.12
AB  - Gross Domestic Product (GDP) known as the pulse of economy for any country depends on multiple factors like export-import, inflation rate and unemployment rate etc. Statistical assessment of GDP demands fresh concepts to explain GDP through its covariates in order to improve and strengthen estimation process. Descriptive analysis for the considered data set from world bank for GDP and covariates is presented through Heatmaps. Identification and relevance of possible set of covariates is done by Ordinary Least Square (OLS) regression followed by Step-wise regression. We propose an alternative statistical algorithm implemented as Bayesian Inference through Integrated Nested Laplace Approximation (INLA) which bridges the gap of accuracy in estimates as opposed to frequentist OLS regression for explaining GDP of country Nepal. Effect of changing prior parameters is assessed through Deviance Information Criterion (DIC). Different scenarios for prior distribution for regression parameters were analyzed to identify most suitable choice of parameter for normal distribution. The comparison of Bayesian and frequentist modelling results is done using several criteria such as Mean Square Error (MSE), Mean Absolute Deviation (MAD) and Mean Absolute Percent Error (MAPE). Bayesian estimation approach is a more efficient method for parametric estimation as compared to OLS classical method. GDP of Nepal was found to have strongest relationship with unemployment rate of Nepal as evident from both classical and Bayesian model.
VL  - 9
IS  - 1
ER  -

Copy | Download