Doombot versus other machine-learning methods for evaluating recession risks in OECD countries:
An extensive literature explains recession risks using a variety of financial and business cycle variables. The problem of selecting a parsimonious set of explanatory variables, which can differ between countries and prediction horizons, is naturally suited to machine-learning methods. The current p...
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Other Authors: | |
Format: | Electronic eBook |
Language: | English |
Published: |
Paris
OECD Publishing
2024
|
Series: | OECD Economics Department Working Papers
no.1821 |
Subjects: | |
Online Access: | DE-862 DE-863 |
Summary: | An extensive literature explains recession risks using a variety of financial and business cycle variables. The problem of selecting a parsimonious set of explanatory variables, which can differ between countries and prediction horizons, is naturally suited to machine-learning methods. The current paper compares models selected by conventional machine-learning methods with a customised algorithm, 'Doombot', which uses 'brute force' to test combinations of variables and imposes restrictions so that predictions are consistent with a coherent economic narrative. The same algorithms are applied to 20 OECD countries with an emphasis on out-of-sample testing using a rolling origin, including a window for the Global Financial Crisis. Despite the imposition of additional restrictions, Doombot is found to the best performing algorithm. Further testing confirms the imposition of judgmental constraints tends to improve rather than hinder out-of-sample performance. Moreover, these constraints provide a more coherent economic narrative and so mitigate the common 'black box' criticism of machine-learning methods. |
Physical Description: | 1 Online-Ressource (41 Seiten) 21 x 28cm. |
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series2 | OECD Economics Department Working Papers |
spelling | Chalaux, Thomas VerfasserIn aut Doombot versus other machine-learning methods for evaluating recession risks in OECD countries Thomas, Chalaux and Dave, Turner Paris OECD Publishing 2024 1 Online-Ressource (41 Seiten) 21 x 28cm. Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier OECD Economics Department Working Papers no.1821 An extensive literature explains recession risks using a variety of financial and business cycle variables. The problem of selecting a parsimonious set of explanatory variables, which can differ between countries and prediction horizons, is naturally suited to machine-learning methods. The current paper compares models selected by conventional machine-learning methods with a customised algorithm, 'Doombot', which uses 'brute force' to test combinations of variables and imposes restrictions so that predictions are consistent with a coherent economic narrative. The same algorithms are applied to 20 OECD countries with an emphasis on out-of-sample testing using a rolling origin, including a window for the Global Financial Crisis. Despite the imposition of additional restrictions, Doombot is found to the best performing algorithm. Further testing confirms the imposition of judgmental constraints tends to improve rather than hinder out-of-sample performance. Moreover, these constraints provide a more coherent economic narrative and so mitigate the common 'black box' criticism of machine-learning methods. Economics Turner, Dave MitwirkendeR ctb |
spellingShingle | Chalaux, Thomas Doombot versus other machine-learning methods for evaluating recession risks in OECD countries Economics |
title | Doombot versus other machine-learning methods for evaluating recession risks in OECD countries |
title_auth | Doombot versus other machine-learning methods for evaluating recession risks in OECD countries |
title_exact_search | Doombot versus other machine-learning methods for evaluating recession risks in OECD countries |
title_full | Doombot versus other machine-learning methods for evaluating recession risks in OECD countries Thomas, Chalaux and Dave, Turner |
title_fullStr | Doombot versus other machine-learning methods for evaluating recession risks in OECD countries Thomas, Chalaux and Dave, Turner |
title_full_unstemmed | Doombot versus other machine-learning methods for evaluating recession risks in OECD countries Thomas, Chalaux and Dave, Turner |
title_short | Doombot versus other machine-learning methods for evaluating recession risks in OECD countries |
title_sort | doombot versus other machine learning methods for evaluating recession risks in oecd countries |
topic | Economics |
topic_facet | Economics |
work_keys_str_mv | AT chalauxthomas doombotversusothermachinelearningmethodsforevaluatingrecessionrisksinoecdcountries AT turnerdave doombotversusothermachinelearningmethodsforevaluatingrecessionrisksinoecdcountries |