Nowcasting GDP: what are the gains from machine learning algorithms?

Milen Arro-Cannarsa and Rolf Scheufele

No 2024-06, Working Papers from Swiss National Bank

Abstract: We compare several machine learning methods for nowcasting GDP. A large mixed-frequency data set is used to investigate different algorithms such as regression based methods (LASSO, ridge, elastic net), regression trees (bagging, random forest, gradient boosting), and SVR. As benchmarks, we use univariate models, a simple forward selection algorithm, and a principal components regression. The analysis accounts for publication lags and treats monthly indicators as quarterly variables combined via blocking. Our data set consists of more than 1,100 time series. For the period after the Great Recession, which is particularly challenging in terms of nowcasting, we find that all considered machine learning techniques beat the univariate benchmark up to 28 % in terms of out-of-sample RMSE. Ridge, elastic net, and SVR are the most promising algorithms in our analysis, significantly outperforming principal components regression.

Keywords: Nowcasting; Forecasting; Machine learning; Rridge; LASSO; Elastic net; Random forest; Bagging; Boosting; SVM; SVR; Large data sets (search for similar items in EconPapers)
JEL-codes: C32 C53 C55 (search for similar items in EconPapers)
Pages: 38 pages
Date: 2024
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ecm and nep-for
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