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Inflation prediction in emerging economies: Machine learning and FX reserves integration for enhanced forecasting

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  • Mirza, Nawazish
  • Rizvi, Syed Kumail Abbas
  • Naqvi, Bushra
  • Umar, Muhammad

Abstract

The present study makes two significant contributions to the extended body of literature in the context of International Finance. First, it forecasts the inflation in an emerging economy by employing a combination of traditional forecasting and Machine Learning models to test whether machine learning models outperform traditional forecasting models. Second, it explicitly includes an often-neglected variable i.e. foreign exchange reserves into the forecasting models to ascertain whether its inclusion enhances predictive accuracy. The outcomes of the study revealed interesting findings. It is observed that machine learning models consistently outperform traditional models, with Random Forest and Gradient Boosting are the top performers across different sets of determinants. Moreover, the study unveils that the inclusion of foreign exchange reserves into the models as a determinant has a positive impact on the predictive effectiveness of both traditional and machine learning-based inflation forecasting models.

Suggested Citation

  • Mirza, Nawazish & Rizvi, Syed Kumail Abbas & Naqvi, Bushra & Umar, Muhammad, 2024. "Inflation prediction in emerging economies: Machine learning and FX reserves integration for enhanced forecasting," International Review of Financial Analysis, Elsevier, vol. 94(C).
  • Handle: RePEc:eee:finana:v:94:y:2024:i:c:s1057521924001704
    DOI: 10.1016/j.irfa.2024.103238
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    1. Joshua Aizenman & Daniel Riera-Crichton, 2008. "Real Exchange Rate and International Reserves in an Era of Growing Financial and Trade Integration," The Review of Economics and Statistics, MIT Press, vol. 90(4), pages 812-815, November.
    2. Helmut Wasserbacher & Martin Spindler, 2022. "Machine learning for financial forecasting, planning and analysis: recent developments and pitfalls," Digital Finance, Springer, vol. 4(1), pages 63-88, March.
    3. N. G. Mankiw, 2009. "The Macroeconomist as Scientist and Engineer," Voprosy Ekonomiki, NP Voprosy Ekonomiki, issue 5.
    4. Gianluca Bontempi & Souhaib Ben Taieb & Yann-Aël Le Borgne, 2013. "Machine learning strategies for time series forecasting," ULB Institutional Repository 2013/167761, ULB -- Universite Libre de Bruxelles.
    5. Inoue, Atsushi & Kilian, Lutz, 2008. "How Useful Is Bagging in Forecasting Economic Time Series? A Case Study of U.S. Consumer Price Inflation," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 511-522, June.
    6. Taylor, John B., 2000. "Low inflation, pass-through, and the pricing power of firms," European Economic Review, Elsevier, vol. 44(7), pages 1389-1408, June.
    7. Vasilios Plakandaras & Theophilos Papadimitriou & Periklis Gogas, 2015. "Forecasting Daily and Monthly Exchange Rates with Machine Learning Techniques," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(7), pages 560-573, November.
    8. Joshua Aizenman & Jaewoo Lee, 2007. "International Reserves: Precautionary Versus Mercantilist Views, Theory and Evidence," Open Economies Review, Springer, vol. 18(2), pages 191-214, April.
    9. Aizenman, Joshua & Edwards, Sebastian & Riera-Crichton, Daniel, 2012. "Adjustment patterns to commodity terms of trade shocks: The role of exchange rate and international reserves policies," Journal of International Money and Finance, Elsevier, vol. 31(8), pages 1990-2016.
    10. Ricardo P. Masini & Marcelo C. Medeiros & Eduardo F. Mendes, 2023. "Machine learning advances for time series forecasting," Journal of Economic Surveys, Wiley Blackwell, vol. 37(1), pages 76-111, February.
    11. George Katona, 1972. "Inflation and the Consumer," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 3(3), pages 788-790.
    12. Qian, Xingwang & Steiner, Andreas, 2017. "International reserves and the maturity of external debt," Journal of International Money and Finance, Elsevier, vol. 73(PB), pages 399-418.
    13. Syed Kumail Abbas Naqvi & Bushra Naqvi, 2010. "Asymmetric Behavior of Inflation Uncertainty and Friedman-Ball Hypothesis: Evidence from Pakistan," Lahore Journal of Economics, Department of Economics, The Lahore School of Economics, vol. 15(2), pages 1-33, Jul-Dec.
    14. Ang, Andrew & Bekaert, Geert & Wei, Min, 2007. "Do macro variables, asset markets, or surveys forecast inflation better?," Journal of Monetary Economics, Elsevier, vol. 54(4), pages 1163-1212, May.
    15. Tschora, Léonard & Pierre, Erwan & Plantevit, Marc & Robardet, Céline, 2022. "Electricity price forecasting on the day-ahead market using machine learning," Applied Energy, Elsevier, vol. 313(C).
    16. Ayesh Ariyasinghe & N. S. Cooray, 2021. "The Nexus Of Foreign Reserves, Exchange Rate And Inflation: Recent Empirical Evidence From Sri Lanka," South Asia Economic Journal, Institute of Policy Studies of Sri Lanka, vol. 22(1), pages 29-72, March.
    17. Yang, Wendong & Sun, Shaolong & Hao, Yan & Wang, Shouyang, 2022. "A novel machine learning-based electricity price forecasting model based on optimal model selection strategy," Energy, Elsevier, vol. 238(PC).
    18. Lutz Kilian, 2009. "Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market," American Economic Review, American Economic Association, vol. 99(3), pages 1053-1069, June.
    19. Chen, Sophia & Ranciere, Romain, 2019. "Financial information and macroeconomic forecasts," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1160-1174.
    20. Blanco, Herminio & Garber, Peter M, 1986. "Recurrent Devaluation and Speculative Attacks on the Mexican Peso," Journal of Political Economy, University of Chicago Press, vol. 94(1), pages 148-166, February.
    21. Olivier Blanchard, 2000. "What Do We Know about Macroeconomics that Fisher and Wicksell Did Not?," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 115(4), pages 1375-1409.
    22. Ahmad Zubaidi Baharumshah & Abdalla Sirag & Norashidah Mohamed Nor, 2017. "Asymmetric Exchange Rate Pass-through in Sudan: Does Inflation React Differently during Periods of Currency Depreciation?," African Development Review, African Development Bank, vol. 29(3), pages 446-457, September.
    23. Aizenman, Joshua & Hutchison, Michael M., 2012. "Exchange market pressure and absorption by international reserves: Emerging markets and fear of reserve loss during the 2008–2009 crisis," Journal of International Money and Finance, Elsevier, vol. 31(5), pages 1076-1091.
    24. James B. Heaton & Nicholas Polson & Jan H. Witte, 2017. "Rejoinder to ‘Deep learning for finance: deep portfolios’," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 33(1), pages 19-21, January.
    25. Yutaka Kurihara & Akio Fukushima, 2019. "AR Model or Machine Learning for Forecasting GDP and Consumer Price for G7 Countries," Applied Economics and Finance, Redfame publishing, vol. 6(3), pages 1-6, May.
    26. Faust, Jon & Wright, Jonathan H., 2013. "Forecasting Inflation," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 2-56, Elsevier.
    27. Gupta, Rangan & Pierdzioch, Christian & Salisu, Afees A., 2022. "Oil-price uncertainty and the U.K. unemployment rate: A forecasting experiment with random forests using 150 years of data," Resources Policy, Elsevier, vol. 77(C).
    28. Dunbar, Kwamie & Owusu-Amoako, Johnson, 2023. "Predicting inflation expectations: A habit-based explanation under hedging," International Review of Financial Analysis, Elsevier, vol. 89(C).
    29. Makridakis, Spyros, 1993. "Accuracy measures: theoretical and practical concerns," International Journal of Forecasting, Elsevier, vol. 9(4), pages 527-529, December.
    30. Periklis Gogas & Theophilos Papadimitriou & Maria Matthaiou & Efthymia Chrysanthidou, 2015. "Yield Curve and Recession Forecasting in a Machine Learning Framework," Computational Economics, Springer;Society for Computational Economics, vol. 45(4), pages 635-645, April.
    31. Akshit Kurani & Pavan Doshi & Aarya Vakharia & Manan Shah, 2023. "A Comprehensive Comparative Study of Artificial Neural Network (ANN) and Support Vector Machines (SVM) on Stock Forecasting," Annals of Data Science, Springer, vol. 10(1), pages 183-208, February.
    32. Mizrach, Bruce, 1992. "The distribution of the Theil U-statistic in bivariate normal populations," Economics Letters, Elsevier, vol. 38(2), pages 163-167, February.
    33. Jaehyun Yoon, 2021. "Forecasting of Real GDP Growth Using Machine Learning Models: Gradient Boosting and Random Forest Approach," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 247-265, January.
    34. Araujo, Gustavo Silva & Gaglianone, Wagner Piazza, 2023. "Machine learning methods for inflation forecasting in Brazil: New contenders versus classical models," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 4(2).
    35. Liang, Chao & Luo, Qin & Li, Yan & Huynh, Luu Duc Toan, 2023. "Global financial stress index and long-term volatility forecast for international stock markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 88(C).
    36. Joshua Aizenman & Nancy P. Marion, 2003. "Foreign exchange reserves in East Asia: why the high demand?," FRBSF Economic Letter, Federal Reserve Bank of San Francisco, issue apr25.
    37. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
    38. Rizvi, S.K.A. & Naqvi, Bushra & Mirza, Nawazish & Bordes, Christian, 2017. "Fear of floating in Asia and the credibility of true floaters?," Research in International Business and Finance, Elsevier, vol. 42(C), pages 149-160.
    39. Mirza, Nawazish & Naqvi, Bushra & Rizvi, Syed Kumail Abbas & Boubaker, Sabri, 2023. "Exchange rate pass-through and inflation targeting regime under energy price shocks," Energy Economics, Elsevier, vol. 124(C).
    40. Khandani, Amir E. & Kim, Adlar J. & Lo, Andrew W., 2010. "Consumer credit-risk models via machine-learning algorithms," Journal of Banking & Finance, Elsevier, vol. 34(11), pages 2767-2787, November.
    41. Edison, Hali J. & Pauls, B. Dianne, 1993. "A re-assessment of the relationship between real exchange rates and real interest rates: 1974-1990," Journal of Monetary Economics, Elsevier, vol. 31(2), pages 165-187, April.
    42. Dąbrowski, Marek A., 2021. "A novel approach to the estimation of an actively managed component of foreign exchange reserves," Economic Modelling, Elsevier, vol. 96(C), pages 83-95.
    43. Kim, Daehwan & Moneta, Fabio, 2021. "Long-term foreign exchange risk premia and inflation risk," International Review of Financial Analysis, Elsevier, vol. 78(C).
    44. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    45. Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios, 2020. "The M4 Competition: 100,000 time series and 61 forecasting methods," International Journal of Forecasting, Elsevier, vol. 36(1), pages 54-74.
    46. Kim, Jong-Min & Kim, Dong H. & Jung, Hojin, 2021. "Applications of machine learning for corporate bond yield spread forecasting," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    47. Syed Kumail Abbas Rizvi & Bushra Naqvi & Christian Bordes & Nawazish Mirza, 2014. "Inflation volatility: an Asian perspective," Economic Research-Ekonomska Istraživanja, Taylor & Francis Journals, vol. 27(1), pages 280-303, January.
    48. Liang, Chao & Wang, Lu & Duong, Duy, 2024. "More attention and better volatility forecast accuracy: How does war attention affect stock volatility predictability?," Journal of Economic Behavior & Organization, Elsevier, vol. 218(C), pages 1-19.
    49. Marcelo C. Medeiros & Gabriel F. R. Vasconcelos & Álvaro Veiga & Eduardo Zilberman, 2021. "Forecasting Inflation in a Data-Rich Environment: The Benefits of Machine Learning Methods," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(1), pages 98-119, January.
    50. Periklis Gogas & Theophilos Papadimitriou & Emmanouil Sofianos, 2022. "Forecasting unemployment in the euro area with machine learning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 551-566, April.
    51. Syed Kumail Abbas Rizvi & Bushra Naqvi & Nawazish Mirza, 2014. "From Fear of Floating to Benign Neglect: The Exchange Rate Regime Roller Coaster in Pakistan," Lahore Journal of Economics, Department of Economics, The Lahore School of Economics, vol. 19(Special E), pages 17-34, September.
    52. Armstrong, J. Scott & Collopy, Fred, 1992. "Error measures for generalizing about forecasting methods: Empirical comparisons," International Journal of Forecasting, Elsevier, vol. 8(1), pages 69-80, June.
    53. Chen, Wei & Xu, Huilin & Jia, Lifen & Gao, Ying, 2021. "Machine learning model for Bitcoin exchange rate prediction using economic and technology determinants," International Journal of Forecasting, Elsevier, vol. 37(1), pages 28-43.
    54. Dai, Zhifeng & Zhang, Xiaotong & Liang, Chao, 2024. "Efficient predictability of oil price: The role of VIX-based panic index shadow line difference," Energy Economics, Elsevier, vol. 129(C).
    55. Giovanni Cicceri & Giuseppe Inserra & Michele Limosani, 2020. "A Machine Learning Approach to Forecast Economic Recessions—An Italian Case Study," Mathematics, MDPI, vol. 8(2), pages 1-20, February.
    56. Léonard Tschora & Erwan Pierre & Marc Plantevit & Céline Robardet, 2022. "Electricity price forecasting on the day-ahead market using machine learning," Post-Print hal-03621974, HAL.
    57. J. B. Heaton & N. G. Polson & J. H. Witte, 2017. "Deep learning for finance: deep portfolios," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 33(1), pages 3-12, January.
    58. He, Qing & Wang, Wenqing & Yu, Jishuang, 2023. "Exchange rate co-movements and corporate foreign exchange exposures: A study on RMB," International Review of Financial Analysis, Elsevier, vol. 90(C).
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    More about this item

    Keywords

    Inflation forecast; Machine learning; Artificial intelligence; FX reserves; International finance; Emerging economy;
    All these keywords.

    JEL classification:

    • C54 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Quantitative Policy Modeling
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • F37 - International Economics - - International Finance - - - International Finance Forecasting and Simulation: Models and Applications
    • F47 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - Forecasting and Simulation: Models and Applications

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