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.
- Sprache
-
Englisch
- Erschienen in
-
Series: Staff Report ; No. 876
- Klassifikation
-
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
- Thema
-
heteroskedasticity
weak identification
robust inference
pretesting
monetary policy
impulse response function
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Lewis, Daniel J.
- Ereignis
-
Veröffentlichung
- (wer)
-
Federal Reserve Bank of New York
- (wo)
-
New York, NY
- (wann)
-
2018
- Handle
- Letzte Aktualisierung
-
20.09.2024, 08:21 MESZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Lewis, Daniel J.
- Federal Reserve Bank of New York
Entstanden
- 2018
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