Machine Learning Macroeconometrics A Primer
Essex Finance Centre Working Papers from University of Essex, Essex Business School
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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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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