Robust inference in structural vector autoregressions with long-run restrictions
Long-run restrictions are a very popular method for identifying structural vector autoregressions, but they suffer from weak identification when the data is very persistent, i.e., when the highest autoregressive roots are near unity. Near unit roots introduce additional nuisance parameters and make...
| Main Authors: | , , |
|---|---|
| Format: | Journal article |
| Language: | English |
| Published: |
Cambridge University Press
2019
|
| _version_ | 1797072690430869504 |
|---|---|
| author | Chevillon, G Mavroeidis, S Zhan, Z |
| author_facet | Chevillon, G Mavroeidis, S Zhan, Z |
| author_sort | Chevillon, G |
| collection | OXFORD |
| description | Long-run restrictions are a very popular method for identifying structural vector autoregressions, but they suffer from weak identification when the data is very persistent, i.e., when the highest autoregressive roots are near unity. Near unit roots introduce additional nuisance parameters and make standard weak-instrument-robust methods of inference inapplicable. We develop a method of inference that is robust to both weak identification and strong persistence. The method is based on a combination of the Anderson-Rubin test with instruments derived by filtering potentially nonstationary variables to make them near stationary using the IVX instrumentation method of Magdalinos and Phillips (2009). We apply our method to obtain robust confidence bands on impulse responses in two leading applications in the literature. |
| first_indexed | 2024-03-06T23:11:17Z |
| format | Journal article |
| id | oxford-uuid:658e5ed7-6bdf-42da-9282-1ed840e46204 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T23:11:17Z |
| publishDate | 2019 |
| publisher | Cambridge University Press |
| record_format | dspace |
| spelling | oxford-uuid:658e5ed7-6bdf-42da-9282-1ed840e462042022-03-26T18:26:16ZRobust inference in structural vector autoregressions with long-run restrictionsJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:658e5ed7-6bdf-42da-9282-1ed840e46204EnglishSymplectic ElementsCambridge University Press2019Chevillon, GMavroeidis, SZhan, ZLong-run restrictions are a very popular method for identifying structural vector autoregressions, but they suffer from weak identification when the data is very persistent, i.e., when the highest autoregressive roots are near unity. Near unit roots introduce additional nuisance parameters and make standard weak-instrument-robust methods of inference inapplicable. We develop a method of inference that is robust to both weak identification and strong persistence. The method is based on a combination of the Anderson-Rubin test with instruments derived by filtering potentially nonstationary variables to make them near stationary using the IVX instrumentation method of Magdalinos and Phillips (2009). We apply our method to obtain robust confidence bands on impulse responses in two leading applications in the literature. |
| spellingShingle | Chevillon, G Mavroeidis, S Zhan, Z Robust inference in structural vector autoregressions with long-run restrictions |
| title | Robust inference in structural vector autoregressions with long-run restrictions |
| title_full | Robust inference in structural vector autoregressions with long-run restrictions |
| title_fullStr | Robust inference in structural vector autoregressions with long-run restrictions |
| title_full_unstemmed | Robust inference in structural vector autoregressions with long-run restrictions |
| title_short | Robust inference in structural vector autoregressions with long-run restrictions |
| title_sort | robust inference in structural vector autoregressions with long run restrictions |
| work_keys_str_mv | AT chevillong robustinferenceinstructuralvectorautoregressionswithlongrunrestrictions AT mavroeidiss robustinferenceinstructuralvectorautoregressionswithlongrunrestrictions AT zhanz robustinferenceinstructuralvectorautoregressionswithlongrunrestrictions |