Macroeconomic Forecasting Using Factor Models and Machine Learning: An Application to Japan

Kohei Maehashi and Mototsugu Shintani
Additional contact information
Kohei Maehashi: School of Engineering, The University of Tokyo
Mototsugu Shintani: Faculty of Economics, The University of Tokyo

No CIRJE-F-1146, CIRJE F-Series from CIRJE, Faculty of Economics, University of Tokyo

Abstract: We perform a thorough comparative analysis of factor models and machine learning to forecast Japanese macroeconomic time series. Our main results can be summarized as follows. First, factor models and machine learning perform better than the con-ventional AR model in many cases. Second, predictions made by machine learning methods perform particularly well for medium to long forecast horizons. Third, the success of machine learning mainly comes from the nonlinearity and interaction ofvariables, suggesting the importance of nonlinear structure in predicting the Japanese macroeconomic series. Fourth, while neural networks are helpful in forecasting, simply adding many hidden layers does not necessarily enhance its forecast accuracy. Fifth, the composite forecast of factor models and machine learning performs better than factor models or machine learning alone, and machine learning methods applied to principal components are found to be useful in the composite forecast.

Pages: 47 pages
Date: 2020-03
New Economics Papers: this item is included in nep-big, nep-cmp, nep-for, nep-ore and nep-pay
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (14)

Downloads: (external link)
http://www.cirje.e.u-tokyo.ac.jp/research/dp/2020/2020cf1146.pdf (application/pdf)

Related works:
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:tky:fseres:2020cf1146

Access Statistics for this paper

More papers in CIRJE F-Series from CIRJE, Faculty of Economics, University of Tokyo Contact information at EDIRC.
Bibliographic data for series maintained by CIRJE administrative office ().