Uniform inference in high-dimensional dynamic panel data models with approximately sparse fixed effects
We establish oracle inequalities for a version of the Lasso in high-dimensional fixed effects dynamic panel data models. The inequalities are valid for the coefficients of the dynamic and exogenous regressors. Separate oracle inequalities are derived for the fixed effects. Next, we show how one can...
| Main Authors: | , |
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| Format: | Journal article |
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Cambridge University Press
2018
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| _version_ | 1797103185280630784 |
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| author | Kock, AB Tang, H |
| author_facet | Kock, AB Tang, H |
| author_sort | Kock, AB |
| collection | OXFORD |
| description | We establish oracle inequalities for a version of the Lasso in high-dimensional fixed effects dynamic panel data models. The inequalities are valid for the coefficients of the dynamic and exogenous regressors. Separate oracle inequalities are derived for the fixed effects. Next, we show how one can conduct uniformly valid inference on the parameters of the model and construct a uniformly valid estimator of the asymptotic covariance matrix which is robust to conditional heteroskedasticity in the error terms. Allowing for conditional heteroskedasticity is important in dynamic models as the conditional error variance may be non-constant over time and depend on the covariates. Furthermore, our procedure allows for inference on high-dimensional subsets of the parameter vector of an increasing cardinality. We show that the confidence bands resulting from our procedure are asymptotically honest and contract at the optimal rate. This rate is different for the fixed effects than for the remaining parts of the parameter vector. |
| first_indexed | 2024-03-07T06:16:30Z |
| format | Journal article |
| id | oxford-uuid:f1412841-82fb-4df5-ac89-b6f1b88ea378 |
| institution | University of Oxford |
| last_indexed | 2024-03-07T06:16:30Z |
| publishDate | 2018 |
| publisher | Cambridge University Press |
| record_format | dspace |
| spelling | oxford-uuid:f1412841-82fb-4df5-ac89-b6f1b88ea3782022-03-27T11:54:38ZUniform inference in high-dimensional dynamic panel data models with approximately sparse fixed effectsJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:f1412841-82fb-4df5-ac89-b6f1b88ea378Symplectic Elements at OxfordCambridge University Press2018Kock, ABTang, HWe establish oracle inequalities for a version of the Lasso in high-dimensional fixed effects dynamic panel data models. The inequalities are valid for the coefficients of the dynamic and exogenous regressors. Separate oracle inequalities are derived for the fixed effects. Next, we show how one can conduct uniformly valid inference on the parameters of the model and construct a uniformly valid estimator of the asymptotic covariance matrix which is robust to conditional heteroskedasticity in the error terms. Allowing for conditional heteroskedasticity is important in dynamic models as the conditional error variance may be non-constant over time and depend on the covariates. Furthermore, our procedure allows for inference on high-dimensional subsets of the parameter vector of an increasing cardinality. We show that the confidence bands resulting from our procedure are asymptotically honest and contract at the optimal rate. This rate is different for the fixed effects than for the remaining parts of the parameter vector. |
| spellingShingle | Kock, AB Tang, H Uniform inference in high-dimensional dynamic panel data models with approximately sparse fixed effects |
| title | Uniform inference in high-dimensional dynamic panel data models with approximately sparse fixed effects |
| title_full | Uniform inference in high-dimensional dynamic panel data models with approximately sparse fixed effects |
| title_fullStr | Uniform inference in high-dimensional dynamic panel data models with approximately sparse fixed effects |
| title_full_unstemmed | Uniform inference in high-dimensional dynamic panel data models with approximately sparse fixed effects |
| title_short | Uniform inference in high-dimensional dynamic panel data models with approximately sparse fixed effects |
| title_sort | uniform inference in high dimensional dynamic panel data models with approximately sparse fixed effects |
| work_keys_str_mv | AT kockab uniforminferenceinhighdimensionaldynamicpaneldatamodelswithapproximatelysparsefixedeffects AT tangh uniforminferenceinhighdimensionaldynamicpaneldatamodelswithapproximatelysparsefixedeffects |