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Dynamic Factor Trees and Forests – A Theory-led Machine Learning Framework for Non-Linear and State-Dependent Short-Term U.S. GDP Growth Predictions

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Abstract
Machine Learning models are often considered to be “black boxes†that provide only little room for the incorporation of theory (cf. e.g. Mukherjee, 2017; Veltri, 2017). This article proposes so-called Dynamic Factor Trees (DFT) and Dynamic Factor Forests (DFF) for macroeconomic forecasting, which synthesize the recent machine learning, dynamic factor model and business cycle literature within a unified statistical machine learning framework for model-based recursive partitioning proposed in Zeileis, Hothorn and Hornik (2008). DFTs and DFFs are non-linear and state-dependent forecasting models, which reduce to the standard Dynamic Factor Model (DFM) as a special case and allow us to embed theory-led factor models in powerful tree-based machine learning ensembles conditional on the state of the business cycle. The out-of-sample forecasting experiment for short-term U.S. GDP growth predictions combines three distinct FRED-datasets, yielding a balanced panel with over 375 indicators from 1967 to 2018 (FRED, 2019; McCracken & Ng, 2016, 2019a, 2019b). Our results provide strong empirical evidence in favor of the proposed DFTs and DFFs and show that they significantly improve the predictive performance of DFMs by almost 20% in terms of MSFE. Interestingly, the improvements materialize in both expansionary and recessionary periods, suggesting that DFTs and DFFs tend to perform not only sporadically but systematically better than DFMs. Our findings are fairly robust to a number of sensitivity tests and hold exciting avenues for future research.

Suggested Citation

  • Daniel Wochner, 2020. "Dynamic Factor Trees and Forests – A Theory-led Machine Learning Framework for Non-Linear and State-Dependent Short-Term U.S. GDP Growth Predictions," KOF Working papers 20-472, KOF Swiss Economic Institute, ETH Zurich.
    • Handle: RePEc:kof:wpskof:20-472
    DOI: 10.3929/ethz-b-000399304
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    Cited by:

    1. Philippe Goulet Coulombe, 2020. "The Macroeconomy as a Random Forest," Papers 2006.12724, arXiv.org, revised Mar 2021.
    2. Philippe Goulet Coulombe, 2021. "The Macroeconomy as a Random Forest," Working Papers 21-05, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management.

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    More about this item

    Keywords

    Forecasting; Machine Learning; Regression Trees and Forests; Dynamic Factor Model; Business Cycles; GDP Growth; United States;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • O47 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Empirical Studies of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence

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