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Forecasting economic activity in data-rich environment

Author

Listed:
  • Maxime Leroux
  • Rachidi Kotchoni
  • Dalibor Stevanovic
Abstract
This paper compares the performance of five classes of forecasting models in an extensive out-of-sample exercise. The types of models considered are standard univariate models, factor-augmented regressions, dynamic factor models, other data-rich models and forecast combinations. These models are compared using four types of data: real series, nominal series, the stock market index and exchange rates. Our Findings can be summarized in a few points: (i) data-rich models and forecasts combination approaches are the best for predicting real series; (ii) ARMA(1,1) model predicts inflation change incredibly well and outperform data-rich models; (iii) the simple average of forecasts is the best approach to predict future SP500 returns; (iv) exchange rates can be predicted at short horizons mainly by univariate models but the random walk dominates at medium and long terms; (v) the optimal structure of forecasting equations changes much over time; and (vi) the dispersion of out-of-sample point forecasts is a good predictor of some macroeconomic and financial uncertainty measures as well as of the business cycle movements among real activity series.

Suggested Citation

  • Maxime Leroux & Rachidi Kotchoni & Dalibor Stevanovic, 2017. "Forecasting economic activity in data-rich environment," EconomiX Working Papers 2017-5, University of Paris Nanterre, EconomiX.
    • Handle: RePEc:drm:wpaper:2017-5
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    References listed on IDEAS

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    Citations

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    Cited by:

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    2. Foroni, Claudia & Marcellino, Massimiliano & Stevanović, Dalibor, 2018. "Mixed frequency models with MA components," Discussion Papers 02/2018, Deutsche Bundesbank.
    3. Philippe Goulet Coulombe, 2020. "Time-Varying Parameters as Ridge Regressions," Papers 2009.00401, arXiv.org, revised Nov 2024.
    4. Philippe Goulet Coulombe, 2020. "To Bag is to Prune," Papers 2008.07063, arXiv.org, revised Sep 2024.

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    More about this item

    Keywords

    Forecasting; Factor Models; Data-rich environment; Model averaging.;
    All these keywords.

    JEL classification:

    • 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
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications

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