Abstract:
Timely updates of business cycle fluctuations-commonly represented by movements in the output gap-help policymakers make informed decisions on the appropriate course of action. Unfortunately, business cycle assessments often suffer from lags in actual gross domestic product data releases. This paper proposes the use of an Economic Activity Index, developed to monitor monthly business cycle fluctuations in Asia. The index summarizes a selection of updated monthly economic indicators to gauge movements in the output gap. The paper shows that the application of machine learning models substantially improves the ability of the index to track actual fluctuations of the business cycle compared with models constructed using a traditional principal component analysis. Grouping the information used to construct the index into six categories-consumption, investment, trade, government, financial, and the external sector- makes it possible to break down and explain drivers of movements in the business cycle.