Structural Vector Autoregressions with Imperfect Identifying Information
- (pp. 466-70)
AbstractThe problem of identification is often the core challenge of empirical economic research. The traditional approach to identification is to bring in additional information in the form of identifying assumptions, such as restrictions that certain magnitudes have to be zero. In this paper, we suggest that what are usually thought of as identifying assumptions should more generally be described as information that the analyst had about the economic structure before seeing the data. Such information is most naturally represented as a Bayesian prior distribution over certain features of the economic structure.
CitationBaumeister, Christiane, and James D. Hamilton. 2022. "Structural Vector Autoregressions with Imperfect Identifying Information." AEA Papers and Proceedings, 112: 466-70. DOI: 10.1257/pandp.20221044
- C32 Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
- C46 Specific Distributions; Specific Statistics