Linear Econometric Models with Machine Learning

Felix Chan and Laszlo Matyas ()

Chapter Chapter 1 in Econometrics with Machine Learning, 2022, pp 1-39 from Springer

Abstract: Abstract This chapter discusses some of the more popular shrinkage estimators in the machine learning literature with a focus on their potential use in econometric analysis. Specifically, it examines their applicability in the context of linear regression models. The asymptotic properties of these estimators are discussed and the implications on statistical inference are explored. Given the existing knowledge of these estimators, the chapter advocates the use of partially penalized methods for statistical inference. Monte Carlo simulations suggest that these methods perform reasonably well. Extensions of these estimators to a panel data setting are also discussed, especially in relation to fixed effects models.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:adschp:978-3-031-15149-1_1

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DOI: 10.1007/978-3-031-15149-1_1

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