Machine Learning Macroeconometrics: A Primer

37 Pages Posted: 28 Sep 2018

See all articles by Dimitris Korobilis

Dimitris Korobilis

University of Glasgow - Adam Smith Business School

Date Written: March 21, 2018

Abstract

This Chapter reviews econometric methods that can be used in order to deal with the challenges of inference in high-dimensional empirical macro models with possibly “more parameters than observations”. These methods broadly include machine learning algorithms for Big Data, but also more traditional estimation algorithms for data with a short span of observations relative to the number of explanatory variables. While building mainly on a univariate linear regression setting, I show how machine learning ideas can be generalized to classes of models that are interesting to applied macroeconomists, such as time-varying parameter models and vector autoregressions.

Keywords: Big Data; Model Selection; Shrinkage; Computation

JEL Classification: C01

Suggested Citation

Korobilis, Dimitris, Machine Learning Macroeconometrics: A Primer (March 21, 2018). Available at SSRN: https://ssrn.com/abstract=3246473 or http://dx.doi.org/10.2139/ssrn.3246473

Dimitris Korobilis (Contact Author)

University of Glasgow - Adam Smith Business School ( email )

40 University Avenue
Gilbert Scott Building
Glasgow, Scotland G12 8QQ
United Kingdom

HOME PAGE: http://https://sites.google.com/site/dimitriskorobilis/

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