A1 Refereed original research article in a scientific journal
Recession forecasting with high-dimensional data
Authors: Nevasalmi Lauri
Publisher: WILEY
Publication year: 2022
Journal: Journal of Forecasting
Journal name in source: JOURNAL OF FORECASTING
Journal acronym: J FORECASTING
Number of pages: 13
ISSN: 0277-6693
eISSN: 1099-131X
DOI: https://doi.org/10.1002/for.2823
Web address : https://doi.org/10.1002/for.2823
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/67835244
Abstract
In this paper, a large amount of different financial and macroeconomic variables are used to predict the U.S. recession periods. We propose a new cost-sensitive extension to the gradient boosting model, which can take into account the class imbalance problem of the binary response variable. The class imbalance, caused by the scarcity of recession periods in our application, is a problem that is emphasized with high-dimensional datasets. Our empirical results show that the introduced cost-sensitive extension outperforms the traditional gradient boosting model in both in-sample and out-of-sample forecasting. Among the large set of candidate predictors, different types of interest rate spreads turn out to be the most important predictors when forecasting U.S. recession periods.
In this paper, a large amount of different financial and macroeconomic variables are used to predict the U.S. recession periods. We propose a new cost-sensitive extension to the gradient boosting model, which can take into account the class imbalance problem of the binary response variable. The class imbalance, caused by the scarcity of recession periods in our application, is a problem that is emphasized with high-dimensional datasets. Our empirical results show that the introduced cost-sensitive extension outperforms the traditional gradient boosting model in both in-sample and out-of-sample forecasting. Among the large set of candidate predictors, different types of interest rate spreads turn out to be the most important predictors when forecasting U.S. recession periods.
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