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.
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Chu, J., Lee, TH., Ullah, A., Wang, R. (2020). Boosting. In: Fuleky, P. (eds) Macroeconomic Forecasting in the Era of Big Data. Advanced Studies in Theoretical and Applied Econometrics, vol 52. Springer, Cham. https://doi.org/10.1007/978-3-030-31150-6_14
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