Lewis, Daniel J;
(2022)
Robust Inference in Models Identified via Heteroskedasticity.
The Review of Economics and Statistics
, 104
(3)
pp. 510-524.
10.1162/rest_a_00963.
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Abstract
Identification via heteroskedasticity exploits variance changes between regimes to identify parameters in simultaneous equations. Weak identification occurs when shock variances change very little or multiple variances change close to proportionally, making standard inference unreliable. I propose an F-test for weak identification in a common simple version of the model. More generally, I establish conditions for validity of nonconservative robust inference on subsets of the parameters, which can be used to test for weak identification. I study monetary policy shocks identified using heteroskedasticity in high-frequency data. I detect weak identification, invalidating standard inference, in daily data, while intraday data provide strong identification.
| Type: | Article |
|---|---|
| Title: | Robust Inference in Models Identified via Heteroskedasticity |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1162/rest_a_00963 |
| Publisher version: | https://doi.org/10.1162/rest_a_00963 |
| Language: | English |
| Additional information: | This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL SLASH UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS > Dept of Economics |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10160481 |
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