Bootstrap Aggregating and Random Forest

Tae Hwy Lee, Aman Ullah and Ran Wang ()
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Ran Wang: University of California, Riverside

No 201918, Working Papers from University of California at Riverside, Department of Economics

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.

Keywords: bagging; decision trees; random forests; forecasting (search for similar items in EconPapers)
JEL-codes: C2 C3 C4 C5 (search for similar items in EconPapers)
Pages: 41 Pages
Date: 2019-07
New Economics Papers: this item is included in nep-big, nep-ecm, nep-ets and nep-ore
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https://economics.ucr.edu/repec/ucr/wpaper/201918.pdf First version, 2019 (application/pdf)

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Persistent link: https://EconPapers.repec.org/RePEc:ucr:wpaper:201918

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