ISSN (Print) - 0012-9976 | ISSN (Online) - 2349-8846

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Do Machine Learning Techniques Provide Better Macroeconomic Forecasts?

Machine learning techniques are now very common in many spheres, and there is a growing popularity of these approaches in macroeconomic forecasting as well. Are these techniques really useful in the prediction of macroeconomic outcomes? Are they superior in performance compared to their traditional counterparts? We carry out a meta-analysis of the existing literature in order to answer these questions. Our analysis suggests that the answers to most of these questions are nuanced and conditional on a number of factors identified in the study.

The authors are grateful to Dweepobotee Brahma and Archana Aggarwal for their helpful comments and discussions.

Timely forecasts of key macroeconomic indicators, such as the gross domestic product (GDP) growth and inflation, are important for policymakers and market participants to gauge the health of the economy, form future expectations, and calibrate their actions. A central banks decision on whether to increase or decrease the monetary policy rate, a governments expectations about the tax revenue it may obtain over the coming year, and decisions regarding investmentsall require forming expectations about the inflation or GDP growth in a country that rests on a foundation of good forecasts.

In recent times, the use of machine learning (to be used interchangeably with the term ML in some places in this article) techniques is gaining popularity in various spheres.1 A limited but growing literature is also emerging that uses these new approaches for macroeconomic forecasts. While most of the research in this field is conducted in the United States (US) and other developed countries central banks, work has been undertaken recently in developing countries as well. However, a broad-based understanding about these approaches is yet to emerge. In this article, we attempt to contribute to a better understanding of these issues by undertaking a critical review of the relevant literature.

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Published On : 20th Jan, 2024

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