Real-Time Monitoring of U.S. Business Cycles via MFD-FM Methodology
Keywords:
Business cycles, Nowcasting, Dynamic factor model, Mixed-frequency data, Kalman filter.Abstract
The objective of this study is to develop a real-time monitoring framework for U.S. business-cycle dynamics using a Mixed-Frequency Dynamic Factor Model (MFD-FM). The methodology combines quarterly GDP with monthly macroeconomic indicators, inflation (CPI), interest rate, housing price index (HPI), and labor force participation rate (LFPR), within a state-space representation estimated through the Kalman filter, producing a latent common factor that summarizes aggregate economic activity. Results indicate that the extracted factor captures cyclical shifts and anticipates downturn and recovery phases associated with recent shocks. The framework is recommended as a macroeconomic surveillance tool to support decision-making under uncertainty. As a limitation, further validation is required across alternative indicator sets and different national contexts, implying possible adjustments for structural breaks and data availability constraints. Originality lies in providing an operational mixed-frequency implementation aimed at real-time cycle monitoring. It is concluded that the proposed MFD-FM approach offers a useful and replicable alternative for timely business-cycle tracking.
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