Do Machine Learning Models Hold the Key to Better Money Demand Forecasting?

Taniya Ghosh and Sakshi Agarwal

A chapter in Environmental, Social, and Governance Perspectives on Economic Development in Asia, 2021, vol. 29A, pp 21-36 from Emerald Group Publishing Limited

Abstract: Significant evidence in the literature points to money demand instability and therefore inaccurate forecasting. In view of this issue, this chapter seeks to use a method, innovative for money demand literature, that is, the machine learning model to predict money demand. Specifically, this chapter uses Random Forest Regression to predict money demand using monthly data in the Indian context over the period April-1996 to December-2018 using the variables usually used in literature. The chapter finds that in money demand prediction, the Random Forest Regression performs fairly well. The results are also compared to traditional models and it is found that the Random Forest Regression model has the potential to enhance the prediction of money demand over what traditional models predicts.

Keywords: Money demand; machine learning models; random forest regression; ARDL; forecasting; monetary policy; C45; C53; E41; E47; E49 (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:eme:isetez:s1571-03862021000029a017

DOI: 10.1108/S1571-03862021000029A017

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