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Forecasting using random subspace methods

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  • Boot, Tom
  • Nibbering, Didier

Abstract

Random subspace methods are a new approach to obtain accurate forecasts in high-dimensional regression settings. Forecasts are constructed by averaging over forecasts from many submodels generated by random selection or random Gaussian weighting of predictors. This paper derives upper bounds on the asymptotic mean squared forecast error of these strategies, which show that the methods are particularly suitable for macroeconomic forecasting. An empirical application to the FRED-MD data confirms the theoretical findings, and shows random subspace methods to outperform competing methods on key macroeconomic indicators.

Suggested Citation

  • Boot, Tom & Nibbering, Didier, 2019. "Forecasting using random subspace methods," Journal of Econometrics, Elsevier, vol. 209(2), pages 391-406.
    • Handle: RePEc:eee:econom:v:209:y:2019:i:2:p:391-406
    DOI: 10.1016/j.jeconom.2019.01.009
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    Cited by:

    1. Mohitosh Kejriwal & Xuewen Yu, 2019. "Generalized Forecasr Averaging in Autoregressions with a Near Unit Root," Purdue University Economics Working Papers 1318, Purdue University, Department of Economics.
    2. Christis Katsouris, 2023. "High Dimensional Time Series Regression Models: Applications to Statistical Learning Methods," Papers 2308.16192, arXiv.org.
    3. Cheng-Feng Wu & Shian-Chang Huang & Chei-Chang Chiou & Tsangyao Chang & Yung-Chih Chen, 2022. "The Relationship Between Economic Growth and Electricity Consumption: Bootstrap ARDL Test with a Fourier Function and Machine Learning Approach," Computational Economics, Springer;Society for Computational Economics, vol. 60(4), pages 1197-1220, December.
    4. Dennis Kant & Andreas Pick & Jasper de Winter, 2022. "Nowcasting GDP using machine learning methods," Working Papers 754, DNB.
    5. Bryan T. Kelly & Asaf Manela & Alan Moreira, 2019. "Text Selection," NBER Working Papers 26517, National Bureau of Economic Research, Inc.
    6. Rachidi Kotchoni & Maxime Leroux & Dalibor Stevanovic, 2019. "Macroeconomic forecast accuracy in a data‐rich environment," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(7), pages 1050-1072, November.

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    More about this item

    Keywords

    Dimension reduction; Forecasting; Random subspace;
    All these keywords.

    JEL classification:

    • 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
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • 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

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