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Machine Learning Macroeconometrics: A Primer

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  • Dimitris Korobilis

    (University of Essex, UK; 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.

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  • Dimitris Korobilis, 2018. "Machine Learning Macroeconometrics: A Primer," Working Paper series 18-30, Rimini Centre for Economic Analysis.
  • Handle: RePEc:rim:rimwps:18-30
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    1. Mark F. J. Steel, 2020. "Model Averaging and Its Use in Economics," Journal of Economic Literature, American Economic Association, vol. 58(3), pages 644-719, September.

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