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Machine Learning Macroeconometrics A Primer

Korobilis, Dimitris (2018) Machine Learning Macroeconometrics A Primer. Working Paper. Essex Finance Centre Working Papers, Colchester.


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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.

Item Type: Monograph (Working Paper)
Uncontrolled Keywords: Big Data; Model Selection; Shrinkage; Computation
Divisions: Faculty of Social Sciences
Faculty of Social Sciences > Essex Business School
Faculty of Social Sciences > Essex Business School > Essex Finance Centre
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 17 Jul 2018 11:11
Last Modified: 06 Jan 2022 13:52

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