• Jianghao Chu
  • Tae-Hwy LeeEmail author
  • Aman Ullah
  • Ran Wang
Part of the Advanced Studies in Theoretical and Applied Econometrics book series (ASTA, volume 52)


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.


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Jianghao Chu
    • 1
  • Tae-Hwy Lee
    • 1
    Email author
  • Aman Ullah
    • 1
  • Ran Wang
    • 1
  1. 1.Department of EconomicsUniversity of CaliforniaRiversideUSA

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