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

Author

Listed:
  • Milen Arro-Cannarsa
  • Dr. Rolf Scheufele

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.

Suggested Citation

  • Milen Arro-Cannarsa & Dr. Rolf Scheufele, 2024. "Nowcasting GDP: what are the gains from machine learning algorithms?," Working Papers 2024-06, Swiss National Bank.
    • Handle: RePEc:snb:snbwpa:2024-06
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    File URL: https://www.snb.ch/en/publications/research/working-papers/2024/working_paper_2024_06
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    More about this item

    Keywords

    Nowcasting; Forecasting; Machine learning; Rridge; LASSO; Elastic net; Random forest; Bagging; Boosting; SVM; SVR; Large data sets;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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