Data and Code for: Structural Vector Autoregressions with Imperfect Identifying Information
Principal Investigator(s): View help for Principal Investigator(s) Christiane Baumeister, University of Notre Dame; James D. Hamilton, University of California-San Diego
Version: View help for Version V1
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Project Citation:
Baumeister, Christiane, and Hamilton, James D. Data and Code for: Structural Vector Autoregressions with Imperfect Identifying Information. Nashville, TN: American Economic Association [publisher], 2022. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2022-04-07. https://doi.org/10.3886/E158141V1
Project Description
Summary:
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The 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. Here we provide the data and code for an empirical illustration of this approach.
Scope of Project
Subject Terms:
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structural vector autoregressions;
Bayesian analysis;
identification;
sign restrictions
JEL Classification:
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C11 Bayesian Analysis: General
C32 Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
Q43 Energy and the Macroeconomy
C11 Bayesian Analysis: General
C32 Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
Q43 Energy and the Macroeconomy
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