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Machine Learning Macroeconometrics: A Primer

Dimitris Korobilis

Working Paper series from Rimini Centre for Economic Analysis

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.

New Economics Papers: this item is included in nep-big
Date: 2018-07
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http://rcea.org/RePEc/pdf/wp18-30.pdf

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Persistent link: https://EconPapers.repec.org/RePEc:rim:rimwps:18-30

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