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  • Tae-Hwy Lee

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

  • Jianghao Chu

    () (University of California, Riverside)

  • Aman Ullah

    () (University of California, Riverside)

  • Ran Wang

    () (University of California, Riverside)


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.

Suggested Citation

  • Tae-Hwy Lee & Jianghao Chu & Aman Ullah & Ran Wang, 2019. "Boosting," Working Papers 201917, University of California at Riverside, Department of Economics.
  • Handle: RePEc:ucr:wpaper:201917

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    More about this item


    Boosting; AdaBoost; Gradient Boosting; Functional Gradient Descent; Decision Tree; Shrinkage;

    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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