Macroeconomic forecasting using factor models and machine learning: an application to Japan

Kohei Maehashi and Mototsugu Shintani

Journal of the Japanese and International Economies, 2020, vol. 58, issue C

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, in many instances, factor models and machine learning perform better than the conventional AR model. 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 of variables, which suggests the importance of nonlinear structure in predicting the Japanese macroeconomic series. Fourth, the composite forecast of factor models and machine learning performs better than factor models or machine learning alone; and machine learning methods applied to common factors are found to be useful in the composite forecast.

Keywords: Lasso; Bagging; Random forests; Boosting; Neural network; Composite forecast (search for similar items in EconPapers)
JEL-codes: C32 C38 C45 C53 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:jjieco:v:58:y:2020:i:c:s0889158320300411

DOI: 10.1016/j.jjie.2020.101104

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