Boosting

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

No 201917, Working Papers from University of California at Riverside, Department of Economics

Abstract: In the era of Big Data, selecting relevant variables from a potentially large pool of candidate variables becomes a newly emerged concern in macroeconomic researches, especially when the data available is high-dimensional, i.e. the number of explanatory variables (p) is greater than the length of the sample size (n). Common approaches include factor models, the principal component analysis and regularized regressions. However, these methods require additional assumptions that are hard to verify and/or introduce biases or aggregated factors which complicate the interpretation of the estimated outputs. This chapter reviews an alternative solution, namely Boosting, which is able to estimate the variables of interest consistently under fairly general conditions given a large set of explanatory variables. Boosting is fast and easy to implement which makes it one of the most popular machine learning algorithms in academia and industry.

Keywords: Boosting; AdaBoost; Gradient Boosting; Functional Gradient Descent; Decision Tree; Shrinkage (search for similar items in EconPapers)
JEL-codes: C2 C3 C4 C5 (search for similar items in EconPapers)
Pages: 34 Pages
Date: 2019-05
New Economics Papers: this item is included in nep-big and nep-ecm
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