CAN MACHINE LEARNING CATCH THE COVID-19 RECESSION?

被引:18
作者
Goulet Coulombe, Philippe [1 ]
Marcellino, Massimiliano [2 ,3 ]
Stevanovic, Dalibor [4 ,5 ]
机构
[1] Univ Penn, Philadelphia, PA 19104 USA
[2] Bocconi Univ, Baffi Carefin, IGIER, BIDSA, Milan, Italy
[3] Bocconi Univ, CEPR, Milan, Italy
[4] Univ Quebec Montreal, Montreal, PQ, Canada
[5] CIRANO, Montreal, PQ, Canada
关键词
machine learning; Big Data; forecasting; Covid-19; NUMBER;
D O I
10.1017/nie.2021.10
中图分类号
F [经济];
学科分类号
02 ;
摘要
Based on evidence gathered from a newly built large macroeconomic dataset (MD) for the UK, labelled UK-MD and comparable to similar datasets for the United States and Canada, it seems the most promising avenue for forecasting during the pandemic is to allow for general forms of nonlinearity by using machine learning (ML) methods. But not all nonlinear ML methods are alike. For instance, some do not allow to extrapolate (like regular trees and forests) and some do (when complemented with linear dynamic components). This and other crucial aspects of ML-based forecasting in unprecedented times are studied in an extensive pseudo-out-of-sample exercise.
引用
收藏
页码:71 / 109
页数:39
相关论文
共 50 条
[31]   Covid-19 analysis by using machine and deep learning [J].
Yadav D. ;
Maheshwari H. ;
Chandra U. ;
Sharma A. .
Studies in Big Data, 2020, 80 :31-63
[32]   A Survey on Machine Learning and Internet of Things for COVID-19 [J].
Elbasi, Ersin ;
Mathew, Shinu ;
Topcu, Ahmet E. ;
Abdelbaki, Wiem .
2021 IEEE WORLD AI IOT CONGRESS (AIIOT), 2021, :115-120
[33]   Machine Learning for Mortality Analysis in Patients with COVID-19 [J].
Sanchez-Montanes, Manuel ;
Rodriguez-Belenguer, Pablo ;
Serrano-Lopez, Antonio J. ;
Soria-Olivas, Emilio ;
Alakhdar-Mohmara, Yasser .
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, 2020, 17 (22) :1-20
[34]   Machine Learning Models to Detect COVID-19: An Overview [J].
Guizani, K. ;
Mejjaouli, S. ;
Guizani, S. .
2022 INTERNATIONAL WIRELESS COMMUNICATIONS AND MOBILE COMPUTING, IWCMC, 2022, :925-930
[35]   Machine learning applications for COVID-19 outbreak management [J].
Heidari, Arash ;
Navimipour, Nima Jafari ;
Unal, Mehmet ;
Toumaj, Shiva .
NEURAL COMPUTING & APPLICATIONS, 2022, 34 (18) :15313-15348
[36]   Significant Applications of Machine Learning for COVID-19 Pandemic [J].
Kushwaha, Shashi ;
Bahl, Shashi ;
Bagha, Ashok Kumar ;
Parmar, Kulwinder Singh ;
Javaid, Mohd ;
Haleem, Abid ;
Singh, Ravi Pratap .
JOURNAL OF INDUSTRIAL INTEGRATION AND MANAGEMENT-INNOVATION AND ENTREPRENEURSHIP, 2020, 5 (04) :453-479
[37]   Battling COVID-19 using machine learning: A review [J].
Chadaga, Krishnaraj ;
Prabhu, Srikanth ;
Vivekananda, Bhat K. ;
Niranjana, S. ;
Umakanth, Shashikiran .
COGENT ENGINEERING, 2021, 8 (01)
[38]   Application of Supervised Machine Learning Techniques to Forecast the COVID-19 U.S. Recession and Stock Market Crash [J].
Rama K. Malladi .
Computational Economics, 2024, 63 :1021-1045
[39]   Comparative analysis of machine learning approaches to analyze and predict the COVID-19 outbreak [J].
Naeem, Muhammad ;
Yu, Jian ;
Aamir, Muhammad ;
Khan, Sajjad Ahmad ;
Adeleye, Olayinka ;
Khan, Zardad .
PEERJ COMPUTER SCIENCE, 2021, 7
[40]   Machine Learning Approach for Forecast Analysis of Novel COVID-19 Scenarios in India [J].
Srivastava, Ankit Kumar ;
Tripathi, Saurabh Mani ;
Kumar, Sachin ;
Elavarasan, Rajvikram Madurai ;
Gangatharan, Sivasankar ;
Kumar, Dinesh ;
Mihet-Popa, Lucian .
IEEE ACCESS, 2022, 10 :95106-95124