Machine Learning in Macroeconomics: Application to DSGE Models

D. M. Nachane and Aditi Chaubal
Additional contact information
D. M. Nachane: Centre for Economic and Social Studies

Chapter Chapter 22 in India’s Contemporary Macroeconomic Themes, 2023, pp 521-541 from Springer

Abstract: Abstract Machine learning—a tool primarily belonging to the statistical, physical, and technological streams—has found its way into the economic sciences too. This paper attempts to conduct a brief review of literature with respect to some of the machine learning tools being used in macroeconomic forecasting, in particular, in DSGE models. The DSGE models have found widespread applicability owing to their firm theoretical micro-foundations in the Real Business Cycle school. Despite their practicality and applicability, these models drew criticism following the global financial crisis broadly due to structural drawbacks of (i) actual economic agents rarely exhibit rational expectations (ii) structure of markets rarely following the efficient market hypothesis, and (iii) aggregation of the behavior of the representative agent to derive macro-processes being true only under restrictive assumptions. This was further augmented by econometric issues of overfitting, simplifying yet false and non-testable identification restrictions, structural (and not practical) specification of the model, etc. The machine learning techniques are applied to attain the objective of reducing dimensionality, and better predictive performance. We then exposit the detailed specifications of two machine learning techniques utilized in studies which implemented the DSGE using machine learning tools, viz., random forests and support vector machines (SVMs), which are used in the classification of the data used in DSGE models. The machine learning techniques applied to classify the data in a DSGE model across studies include the random forests, SVMs, neural networks, logistic regressions, etc. The paper concludes with the implications that the introduction of the machine learning techniques has on DSGE models, and the policy implications thereof.

Keywords: Machine learning; Random forests; Support vector machine; DSGE models; Macroeconomic forecasting; C10; C45; C50; C53; C60 (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

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:spr:isbchp:978-981-99-5728-6_22

Ordering information: This item can be ordered from
http://www.springer.com/9789819957286

DOI: 10.1007/978-981-99-5728-6_22

Access Statistics for this chapter

More chapters in India Studies in Business and Economics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().