Data and Code for: Signing Out Confounding Shocks in Variance-Maximizing Identification Methods
Principal Investigator(s): View help for Principal Investigator(s) Neville Francis, University of North Carolina-Chapel Hill; Gene Kindberg-Hanlon, International Monetary Fund
Version: View help for Version V1
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Project Citation:
Francis, Neville, and Kindberg-Hanlon, Gene. Data and Code for: Signing Out Confounding Shocks in Variance-Maximizing Identification Methods. Nashville, TN: American Economic Association [publisher], 2022. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2022-05-04. https://doi.org/10.3886/E168681V1
Project Description
Summary:
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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 auto-regression. Applying this to U.S. data we find a broadly equal role
for demand and supply shocks in generating business-cycle fluctuations.
Scope of Project
Subject Terms:
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SVARs;
business cycles
JEL Classification:
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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
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
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