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ECONOMIC RECOVERY AFTER COVID-19: AN ASSESSMENT OF SELECTED G20 COUNTRIES
Keywords:
COVID-19, supply chain, inflation, unemployment, machine learning, deep learning, LSTM, XGBoost.DOI:
https://doi.org/10.17654/0972361723070Abstract
The COVID-19 pandemic has profoundly impacted global economics; now, there are concerns about the potential onset of a recession as central banks raise interest rates to combat the persistent inflation problem. This study employed a comparative approach, focusing on G20 countries, to analyze the likelihood of a recession in the aftermath of the COVID-19 crisis.
Using LSTM (long short-term memory) and extreme gradient boosting (XGBoost), we investigated the economic performance of selected G20 countries via various macroeconomic variables, e.g., real GDP growth, unemployment rates, inflation, and interest rates. More specifically, we did a comparative analysis of long short-term memory approaches that will be used to explain possible downturns in their economies. The actual versus forecasted values showed a robust fit, as evidenced by the root mean square error (RMSE). Each country had different forecasted values beyond the actual data. The empirical results showed mixed results, and we expected negative growth rates across the selected G20 countries due to rapid inflation. However, some countries like South Africa, Indonesia, and Japan showed consistent positive growth despite inflationary pressures. The United States, on the other hand, indicated a slower growth rate but also showed a positive rebound.
Received: July 15, 2023
Revised: November 15, 2023
Accepted: November 18, 2023
References
A. Alexandrov et al., GluonTS: probabilistic and neural time series modeling in Python, J. Mach. Learn. Res. 21(1) (2020), 4629-4634.
F. Alvarez, D. Argente and F. Lippi, A simple planning problem for covid-19 lockdown1, National Bureau of Economic Research (w26981), 2020.
Z. A. Bethune and A. Korinek, Covid-19 infection externalities: trading off lives vs. livelihoods, Technical Report w27009, National Bureau of Economic Research, 2020.
M. A. Bolhuis and B. Rayner, Deus ex machina? A framework for macro forecasting with machine learning (International Monetary Fund), 2020.
B. Bonadio, Z. Huo and N. Pandalai-Nayar, Global supply chains in the pandemic, Technical Report 14766, CEPR Discussion Papers, 2020.
L. Breiman, Random forests, Machine Learning 45 (2001), 5-32.
C. J. Burges, From RankNet to LambdaRank to LambdaMART: an overview, Learning 11(23-581) (2010), 81.
V. Cerra and S. C. Saxena, Covid-19 coronavirus and macroeconomic policy, 2008.
https://repositori.upf.edu/bitstream/handle/10230/48498/CREIFornaroWolfCorona v2.pdf?sequence1&isAllowedy.
C. Chakraborty and A. Joseph, Machine learning at central banks, Technical report, Bank of England, 2017.
T. Chen and C. Guestrin, XGBoost: reliable large-scale tree boosting system, Proceedings of the 22nd SIGKDD Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, 2016, pp. 785-794.
T. Drechsel and S. Kalemli-Özcan, Are standard macro and credit policies enough to deal with the economic fallout from a global pandemic? A proposal for a negative SME tax, The University of Maryland, 2020.
R. E. Fan, K. W. Chang, C. J. Hsieh, X. R. Wang and C. J. Lin, LIBLINEAR: a library for large linear classification, J. Mach. Learn. Res. 9 (2008), 1871-1874.
L. Fornaro and M. Wolf, Covid-19 coronavirus and macroeconomic policy, 2020. https://repositori.upf.edu/bitstream/handle/10230/48498/CREIFornaroWolfCorona v2.pdf?sequence1&isAllowedy.
J. H. Friedman, Greedy function approximation: a gradient boosting machine, Ann. Statist. 29 (2001), 1189-1232.
P. O. Gourinchas, Flattening the pandemic and recession curves, Mitigating the COVID Economic Crisis: Act Fast and Do Whatever 31(2) (2020), 57-62.
V. Guerrieri, G. Lorenzoni, L. Straub and I. Werning, Macroeconomic implications of covid-19: can negative supply shocks cause demand shortages? American Economic Review 112(5) (2022), 1437-1474.
S. Hochreiter and J. Schmidhuber, Long short-term memory, Neural Comput. 9(8) (1997), 1735-1780.
R. Johnson and T. Zhang, Learning nonlinear functions using regularized greedy forest, IEEE Transactions on Pattern Analysis and Machine Intelligence 36(5) (2013), 942-954.
O. Jorda, S. Singh and A. Taylor, Longer-run economic consequences of pandemics, Technical Report w26934, National Bureau of Economic Research, Inc., 2020.
M. I. Jordan and T. M. Mitchell, Machine learning: trends, perspectives, and prospects, Science 349(6245) (2015), 255-260.
J. K. Jung, M. Patnam and A. Ter-Martirosyan, An algorithmic crystal ball: forecasts-based on machine learning, International Monetary Fund, 2018.
L. Laeven and F. Valencia, Systemic banking crises database, IMF Economic Review 61(2) (2013), 225-270.
Y. LeCun, Y. Bengio and G. Hinton, Deep learning, Nature 521(7553) (2015), 436-444.
B. Lim and S. Zohren, Time-series forecasting with deep learning: a survey, Philosophical Transactions of the Royal Society A 379(2194) (2021), Paper No. 20200209, 14 pp.
C. Ma, J. H. Rogers and S. Zhou, Modern pandemics: recession and recovery, Technical report, BOFIT Discussion, 2020.
https://nbn-resolving.de/urn:NBN:fi:bof-20200703222.
M. W. McCracken and S. Ng, Fred-md: a monthly database for macroeconomic research, J. Bus. Econom. Statist. 34(4) (2016), 574-589.
M. C. Medeiros, G. F. Vasconcelos, Á. Veiga and E. Zilberman, Forecasting inflation in a data-rich environment: the benefits of machine learning methods, J. Bus. Econom. Statist. 39(1) (2021), 98-119.
H. T. Nguyen, D. T. Nguyen, N. T. Nguyen, H. M. Nguyen, V. H. Nguyen, N. N. Tan, H. M. Nguyen, H. N. Truong and N. T. X. Linh, Macroeconomic forecasting based on LSTM-conditioned normalizing flows, Prediction and Causality in Econometrics and Related Topics, 2022, pp. 658-669.
C. Olah, Understanding LSTM, 2015.
https://colah.github.io/posts/2015-08-Understanding-LSTMs/,colahsBlog.
C. M. Reinhart and K. S. Rogoff, The aftermath of financial crises, American Economic Review 99(2) (2009), 466-472.
A. Smalter Hall and T. R. Cook, Macroeconomic indicator forecasting with deep neural networks, Federal Reserve Bank of Kansas City Working Paper (17-11), 2017.
https://www.kansascityfed.org/Research%20Working%20Papers/documents/ 4065/rwp17-11cooksmalterhall.pdf.
P. C. Szlezak, P. Swartz and M. Reeves, Weighing the Risk of Inflation, Recession and Stagflation in the U.S. Economy, Harvard Business Review, Cambridge, Harvard, 2022.
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