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Bootstrap Aggregating and Random Forest

Author

Listed:
  • Tae-Hwy Lee

    () (Department of Economics, University of California Riverside)

  • Aman Ullah

    () (University of California, Riverside)

  • Ran Wang

    () (University of California, Riverside)

Abstract

Bootstrap Aggregating (Bagging) is an ensemble technique for improving the robustness of forecasts. Random Forest is a successful method based on Bagging and Decision Trees. In this chapter, we explore Bagging, Random Forest, and their variants in various aspects of theory and practice. We also discuss applications based on these methods in economic forecasting and inference.

Suggested Citation

  • Tae-Hwy Lee & Aman Ullah & Ran Wang, 2019. "Bootstrap Aggregating and Random Forest," Working Papers 201918, University of California at Riverside, Department of Economics.
  • Handle: RePEc:ucr:wpaper:201918
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    File URL: https://economics.ucr.edu/repec/ucr/wpaper/201918.pdf
    File Function: First version, 2019
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    More about this item

    Keywords

    bagging; decision trees; random forests; forecasting;

    JEL classification:

    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling

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