  In the recent years more and more high-dimensional data sets, where the
number of parameters $p$ is high compared to the number of observations $n$ or
even larger, are available for applied researchers. Boosting algorithms
represent one of the major advances in machine learning and statistics in
recent years and are suitable for the analysis of such data sets. While Lasso
has been applied very successfully for high-dimensional data sets in Economics,
boosting has been underutilized in this field, although it has been proven very
powerful in fields like Biostatistics and Pattern Recognition. We attribute
this to missing theoretical results for boosting. The goal of this paper is to
fill this gap and show that boosting is a competitive method for inference of a
treatment effect or instrumental variable (IV) estimation in a high-dimensional
setting. First, we present the $L_2$Boosting with componentwise least squares
algorithm and variants which are tailored for regression problems which are the
workhorse for most Econometric problems. Then we show how $L_2$Boosting can be
used for estimation of treatment effects and IV estimation. We highlight the
methods and illustrate them with simulations and empirical examples. For
further results and technical details we refer to Luo and Spindler (2016, 2017)
and to the online supplement of the paper.

Machine Learning;Econometrics;Methodology