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DTSTART:20220327T010000
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DTSTART;TZID=Europe/Helsinki:20220512T140000
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SUMMARY:Jannis KUECK  (Universität Hamburg) "Estimation and Inference of Treatment Effects with L2-Boosting in High-Dimensional  Settings"
DESCRIPTION:The Economics and Statistics  Seminar:\nTime: 14:00 pm – 15.15 pm\nDate: 12 th of May 2022\nRoom 3001 \nJannis KUECK (Universität Hamburg) “Estimation and Inference of Treatment Effects with L2-Boosting in High-Dimensional Settings” \nAbstract : Empirical researchers are increasingly faced with rich data sets containing many controls or instrumental variables\, making it essential to choose an appropriate approach to variable selection. In this paper\, we provide results for valid inference after post- or orthogonal L2-Boosting is used for variable selection. We consider treatment effects after selecting among many control variables and instrumental variable models with potentially many instruments. To achieve this\, we establish new results for the rate of convergence of iterated post-L2-Boosting and orthogonal L2-Boosting in a high-dimensional setting similar to Lasso\, i.e.\, under approximate sparsity without assuming the beta-min condition. These results are extended to the 2SLS framework and valid inference is provided for treatment effect analysis. We give extensive simulation results for the proposed methods and compare them with Lasso. In an empirical application\, we construct efficient IVs with our proposed methods to estimate the effect of pre-merger overlap of bank branch networks in the US on the post-merger stock returns of the acquirer bank \nJoint work : Ye LUO\, Martin SPINDLER\, Zigan WANG \nOrganizers:\nPhilippe CHONE (CREST) \nSponsors:\nCREST \n
URL:https://econ.ip-paris.fr/event/jannis-kueck-universitat-hamburg-estimation-and-inference-of-treatment-effects-with-l2-boosting-in-high-dimensional-settings/
CATEGORIES:Economics
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