EconPapers    
Economics at your fingertips  
 

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.

New Economics Papers: this item is included in nep-big and nep-ecm
Date: 2018-07
References: Add references at CitEc
Citations: View citations in EconPapers (1) Track citations by RSS feed

Downloads: (external link)
http://repository.essex.ac.uk/22666/ original version (application/pdf)

Related works:
Working Paper: Machine Learning Macroeconometrics: A Primer (2018) Downloads
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:esy:uefcwp:22666

Access Statistics for this paper

More papers in Essex Finance Centre Working Papers from University of Essex, Essex Business School Contact information at EDIRC.
Bibliographic data for series maintained by Nikolaos Vlastakis ().

 
Page updated 2019-11-16
Handle: RePEc:esy:uefcwp:22666