24 mai 2022
Séminaire Banque de France
Résumé
A key question for households, firms, and policy makers is: how is the economy doing now? We develop a Bayesian dynamic factor model and compute daily estimates of US GDP growth. Our framework gives prominence to features of modern business cycles absent in linear Gaussian models, including movements in long-run growth, time-varying uncertainty, and fat tails. We also incorporate newly available high-frequency data on consumer behavior. The model beats benchmark econometric models and survey expectations at predicting GDP growth over two decades, and advances our understanding of macroeconomic data during the recession of spring 2020.
Mots-clés
Nowcasting; Daily economic index; Dynamic factor models; Real-time data; Bayesian Methods; Fat Tails.;
Codes JEL
- E32: Business Fluctuations • Cycles
- E23: Production
- O47: Measurement of Economic Growth • Aggregate Productivity • Cross-Country Output Convergence
- C32: Time-Series Models • Dynamic Quantile Regressions • Dynamic Treatment Effect Models • Diffusion Processes
- E01: Measurement and Data on National Income and Product Accounts and Wealth • Environmental Accounts