Arbeitspapier
Robust inference in models identified via heteroskedasticity
Identification via heteroskedasticity exploits differences in variances across regimes to identify parameters in simultaneous equations. I study weak identification in such models, which arises when variances change very little or the variances of multiple shocks change close to proportionally. I show that this causes standard inference to become unreliable, propose two tests to detect weak identification, and develop nonconservative methods for robust inference on a subset of the parameter vector. I apply these tools to monetary policy shocks, identified using heteroskedasticity in high frequency data. I detect weak identification in daily data, causing standard inference methods to be invalid. However, using intraday data instead allows the shocks to be strongly identified.
- Language
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Englisch
- Bibliographic citation
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Series: Staff Report ; No. 876
- Classification
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Wirtschaft
Hypothesis Testing: General
Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
Interest Rates: Determination, Term Structure, and Effects
- Subject
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heteroskedasticity
weak identification
robust inference
pretesting
monetary policy
impulse response function
- Event
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Geistige Schöpfung
- (who)
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Lewis, Daniel J.
- Event
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Veröffentlichung
- (who)
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Federal Reserve Bank of New York
- (where)
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New York, NY
- (when)
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2018
- Handle
- Last update
-
10.03.2025, 11:41 AM CET
Data provider
This object is provided by:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Lewis, Daniel J.
- Federal Reserve Bank of New York
Time of origin
- 2018
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