Local Projection Inference in High Dimensions
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2022
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| _version_ | 1866910412155387904 |
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| author | Adamek, Robert Smeekes, Stephan Wilms, Ines |
| author_facet | Adamek, Robert Smeekes, Stephan Wilms, Ines |
| contents | In this paper, we estimate impulse responses by local projections in high-dimensional settings. We use the desparsified (de-biased) lasso to estimate the high-dimensional local projections, while leaving the impulse response parameter of interest unpenalized. We establish the uniform asymptotic normality of the proposed estimator under general conditions. Finally, we demonstrate small sample performance through a simulation study and consider two canonical applications in macroeconomic research on monetary policy and government spending. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2209_03218 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Local Projection Inference in High Dimensions Adamek, Robert Smeekes, Stephan Wilms, Ines Econometrics Statistics Theory Applications Methodology In this paper, we estimate impulse responses by local projections in high-dimensional settings. We use the desparsified (de-biased) lasso to estimate the high-dimensional local projections, while leaving the impulse response parameter of interest unpenalized. We establish the uniform asymptotic normality of the proposed estimator under general conditions. Finally, we demonstrate small sample performance through a simulation study and consider two canonical applications in macroeconomic research on monetary policy and government spending. |
| title | Local Projection Inference in High Dimensions |
| topic | Econometrics Statistics Theory Applications Methodology |
| url | https://arxiv.org/abs/2209.03218 |