Machine Learning Macroeconometrics A Primer

Dimitris Korobilis

Essex Finance Centre Working Papers from University of Essex, Essex Business School

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 (search for similar items in EconPapers)
Date: 2018-07
New Economics Papers: this item is included in nep-big and nep-ecm
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Citations: View citations in EconPapers (2)

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Working Paper: Machine Learning Macroeconometrics: A Primer (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:esy:uefcwp:22666

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