Instrumental Variable Estimation of Structural VAR Models Robust to Possible Non-Stationarity
Research output: Conference Papers › RGC 33 - Other conference paper › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Pages | 1-35 |
Number of pages | 35 |
Publication status | Presented - 15 Aug 2019 |
Conference
Title | 2019 NBER-NSF Time Series Conference |
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Location | Chinese University of Hong Kong |
Place | China |
City | Hong Kong |
Period | 14 - 15 August 2019 |
Link(s)
Permanent Link | https://scholars.cityu.edu.hk/en/publications/publication(21b1f9ef-a5e1-407a-b942-188e31ba3575).html |
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Abstract
This paper considers the estimation of dynamic causal effects using external instruments and a structural vector-autoregressive model with possibly non-stationary regressors. We provide general conditions under which the asymptotic normal approximation remains valid. In this case, the asymptotic variance depends on the persistence property of each series. We further provide a consistent asymptotic covariance matrix estimator that requires neither such knowledge nor pre-tests for nonstationarity. The proposed consistent covariance matrix estimator is robust and is easy to implement in practice.
Research Area(s)
- external instruments, non-stationarity, robust inference, structural VAR
Bibliographic Note
Information for this record is supplemented by the author(s) concerned.
Citation Format(s)
Instrumental Variable Estimation of Structural VAR Models Robust to Possible Non-Stationarity. / HAN, Xu; INOUE, Atsushi; Cheng, Xu.
2019. 1-35 2019 NBER-NSF Time Series Conference, Hong Kong, China.
2019. 1-35 2019 NBER-NSF Time Series Conference, Hong Kong, China.
Research output: Conference Papers › RGC 33 - Other conference paper › peer-review