Conceptualization of Machine Learning in Economic Forecasting

Authors

  • Harish Paruchuri Anthem, Inc.

DOI:

https://doi.org/10.18034/abr.v11i2.532

Keywords:

Machine Learning, Economic Forecasting, decision making, Italian Economy

Abstract

Economic forecasting is a very important aspect that policymakers in the financial and corporate organization rely on because helps them to determine future events that might infringe some hardship on the economy and the citizens at large. However, the principal statistical pointers that are available to the public domain provide numerous reservations and doubts for their economics estimates as it is later released with frequent issues to major revisions and also it shows a great lag in decision making for an incoming event. To this effect, the expansion of the latest forecasting patterns was important to address the gaps. Hence, this paper examines the conceptualization of Machine learning in economic forecasting. To achieve this, the Italian economy was used as the dataset, and machine learning controlled tools were used as the method of analysis. The result obtained from this study shows that machine learning is a better model to use in economic forecasting for quick and reliable data to avert future events.

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Author Biography

  • Harish Paruchuri, Anthem, Inc.

    Senior AI Engineer, Department of Information Technology, Anthem, Inc., USA

References

Ahmed, A. A. A., Aljarbouh, A., Donepudi, P. K., & Choi, M. S. (2021). Detecting Fake News using Machine Learning: A Systematic Literature Review. Psychology and Education, 58(1), 1932–1939. https://zenodo.org/record/4494366. https://doi.org/10.5281/zenodo.4494366

Breiman, L. (2001). Statistical modeling: The two cultures (with comments and a rejoinder by the author). Stat. Sci. 16, 199–231.

Cicceri, G., Inserra, G. and Limosani, M. (2020). A Machine Learning Approach to Forecast Economic Recessions—An Italian Case Study. Mathematics, 8(24), 1-20.

Dolfin, M., Knopoff, D., Limosani, M. and Xibilia, M. G. (2019a). Credit Risk Contagion and Systemic Risk on Networks. Mathematics, 7, 713.

Dolfin, M., Leonida, L. and Muzzupappa, E. (2019b). Forecasting Efficient Risk/Return Frontier for Equity Risk with a KTAP Approach—A Case Study in Milan Stock Exchange. Symmetry, 11, 1055.

Dolfin, M., Leonida, L. and Outada, N. (2017). Modeling human behavior in economics and social science. Phys. Life Rev., 22, 1–21.

Donepudi, P. K., Ahmed, A. A. A., Hossain, M. A., & Maria, P. (2020b). Perceptions of RAIA Introduction by Employees on Employability and Work Satisfaction in the Modern Agriculture Sector. International Journal of Modern Agriculture, 9(4), 486–497. https://doi.org/10.5281/zenodo.4428205

Donepudi, P. K., Banu, M. H., Khan, W., Neogy, T. K., Asadullah, ABM., & Ahmed, A. A. A. (2020a). Artificial Intelligence and Machine Learning in Treasury Management: A Systematic Literature Review. International Journal of Management, 11(11), 13–22. https://doi.org/10.5281/zenodo.4247297

Fildes, R. and Stekler, H. (2002). The state of macroeconomic forecasting. J. Macroecon., 24, 435–468.

Gavin, H. (2011). The Levenberg-Marquardt Method for Nonlinear Least Squares Curve-Fitting Problems. Ph.D. Thesis, Department of Civil and Environmental Engineering, Duke University, Durham, NC, USA, pp. 1–15.

Jansen, W. J.; Jin, X. and de Winter, J. M. (2016). Forecasting and nowcasting real GDP: Comparing statistical models and subjective forecasts. Int. J. Forecast., 32, 411– 436.

McCracken, M. W. and Ng, S. (2016). Fred-md: A monthly database for macroeconomic research. Journal of Business and Economic Statistics, 34(4):574–589.

Mullainathan, S. and Spiess, J. (2017). Machine learning: An applied econometric approach. J. Econ. Perspect, 31, 87–106.

Nyman, R. and Ormerod, P. 2017. Predicting Economic Recessions Using Machine Learning Algorithms. ArXiv: 1701.01428.

OECD Stat. (2019). https://stats.oecd.org/

Paruchuri, H. (2017). Credit Card Fraud Detection using Machine Learning: A Systematic Literature Review. ABC Journal of Advanced Research, 6(2), 113-120. https://doi.org/10.18034/abcjar.v6i2.547

Paruchuri, H. (2019). Market Segmentation, Targeting, and Positioning Using Machine Learning. Asian Journal of Applied Science and Engineering, 8(1), 7-14. Retrieved from https://journals.abc.us.org/index.php/ajase/article/view/1193

Paruchuri, H. (2020). The Impact of Machine Learning on the Future of Insurance Industry. American Journal of Trade and Policy, 7(3), 85-90. https://doi.org/10.18034/ajtp.v7i3.537

Prüser, J. (2019). Forecasting with many predictors using Bayesian additive regression trees. J. Forecast., 38, 621–631.

Sahm, C. (2019). Direct Stimulus Payments to Individuals. In Recession Ready: Fiscal Policies to Stabilize the American Economy; Boushey, H., Nunn, R., Shambaugh, J., Eds. The Hamilton Project and the Washington Center for Equitable Growth: Washington, DC, USA,

Vadlamudi, S. (2019). How Artificial Intelligence Improves Agricultural Productivity and Sustainability: A Global Thematic Analysis. Asia Pacific Journal of Energy and Environment, 6(2), 91-100. https://doi.org/10.18034/apjee.v6i2.542

Vadlamudi, S. (2020a). Internet of Things (IoT) in Agriculture: The Idea of Making the Fields Talk. Engineering International, 8(2), 87-100. https://doi.org/10.18034/ei.v8i2.522

Vadlamudi, S. (2020b). The Impacts of Machine Learning in Financial Crisis Prediction. Asian Business Review, 10(3), 171-176. https://doi.org/10.18034/abr.v10i3.528

Vadlamudi, S.; Paruchuri, H.; Ahmed, A. A. A.; Hossain, M. S.; & Donepudi, P. K. (2021). Rethinking Food Sufficiency with Smart Agriculture using Internet of Things. Turkish Journal of Computer and Mathematics Education, 12(9), 2541–2551. https://turcomat.org/index.php/turkbilmat/article/view/3738

Wu, Z.; Durand, H. and Christofides, P.D. (2018). Safeness Index-Based Economic Model Predictive Control of Stochastic Nonlinear Systems. Mathematics, 6, 69.

Zhu, Y., Kamal, E. M., Gao, G., Ahmed, A. A. A., Asadullah, A., Donepudi, P. K. (2021). Excellence of Financial Reporting Information and Investment Productivity. International Journal of Nonlinear Analysis and Applications, 12(1), 75-86. https://doi.org/10.22075/ijnaa.2021.4659

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Published

2021-05-01

How to Cite

Paruchuri, H. (2021). Conceptualization of Machine Learning in Economic Forecasting. Asian Business Review, 11(2), 51-58. https://doi.org/10.18034/abr.v11i2.532

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