2024-03-28T19:50:00
118082
Thu Mar 28 19:50:02 EDT 2024
Data and Code for: Business Cycle Anatomy
Marios Angeletos
Fabrice Collard
Harris Dellas
118082
https://doi.org/10.3886/E118082V1
We propose a new strategy for dissecting the macroeconomic time series, provide a template for the business-cycle propagation mech- anism that best describes the data, and use its properties to ap- praise models of both the parsimonious and the medium-scale va- riety. Our findings support the existence of a main business-cycle driver but rule out the following candidates for this role: technology or other shocks that map to TFP movements; news about future productivity; and inflationary demand shocks of the textbook type. Models aimed at accommodating demand-driven cycles without a strict reliance on nominal rigidity appear promising.
Business Cycle
VectorAutoregression
Shocks
C32 Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
E32 Business Fluctuations; Cycles
United States
1955 – 2017
Federal Reserve Economic Database