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Andrian Yambolov
- 20 August 2025
- WORKING PAPER SERIES - No. 3100Details
- Abstract
- When economic analysis requires simultaneous inference across multiple variables and time horizons, this paper shows that conventional pointwise quantiles in Bayesian structural vector autoregressions significantly understate the uncertainty of impulse responses. The performance of recently proposed joint inference methods, which produce noticeably different error band estimates, is evaluated, and calibration routines are suggested to ensure that they achieve the intended nominal probability coverage. Two practical applications illustrate the implications of these findings: (i) within a structural vector autoregression, the fiscal multiplier exhibits error bands that are 51% to 91% wider than previous estimates, and (ii) a pseudo-out-of-sample projection exercise for inflation and gross domestic product shows that joint inference methods could effectively summarize uncertainty for forecasts as well. These results underscore the importance of using joint inference methods for more robust econometric analysis.
- JEL Code
- C22 : Mathematical and Quantitative Methods→Single Equation Models, Single Variables→Time-Series Models, Dynamic Quantile Regressions, Dynamic Treatment Effect Models &bull Diffusion Processes
C32 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Time-Series Models, Dynamic Quantile Regressions, Dynamic Treatment Effect Models, Diffusion Processes
C52 : Mathematical and Quantitative Methods→Econometric Modeling→Model Evaluation, Validation, and Selection