Signing Out Confounding Shocks in Variance-Maximizing Identification Methods
- (pp. 476-80)
Abstract
Recent papers have examined the dominant drivers of business cycles using variance-maximizing techniques for identification. However, identification is poor when shocks other than the target of interest play large roles in driving volatility at the targeted frequency or horizon, leading them to capture a "hybrid" shock. This paper suggests a simple fix that lowers biases in the impulse responses. The fix is to include theoretically informed sign and magnitude restrictions at the identification stage of the vector autoregression. Applying this to US data, we find an equal role for demand and supply shocks in generating business cycle fluctuations.Citation
Francis, Neville, and Gene Kindberg-Hanlon. 2022. "Signing Out Confounding Shocks in Variance-Maximizing Identification Methods." AEA Papers and Proceedings, 112: 476-80. DOI: 10.1257/pandp.20221046Additional Materials
JEL Classification
- C32 Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
- E23 Macroeconomics: Production
- E32 Business Fluctuations; Cycles