Essays in econometrics and machine learning

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Economics, 2018 === Cataloged from PDF version of thesis. === Includes bibliographical references (pages 209-213). === Establishing the link between a cause and effect is a fundamental question in social science. Standard assumptio...

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Main Author: Semenova, Vira.
Other Authors: Victor Chernozhukov, Whitney Newey and Anna Mikusheva.
Format: Others
Language:English
Published: Massachusetts Institute of Technology 2019
Subjects:
Online Access:https://hdl.handle.net/1721.1/122542
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spelling ndltd-MIT-oai-dspace.mit.edu-1721.1-1225422019-10-15T03:15:06Z Essays in econometrics and machine learning Semenova, Vira. Victor Chernozhukov, Whitney Newey and Anna Mikusheva. Massachusetts Institute of Technology. Department of Economics. Massachusetts Institute of Technology. Department of Economics Economics. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Economics, 2018 Cataloged from PDF version of thesis. Includes bibliographical references (pages 209-213). Establishing the link between a cause and effect is a fundamental question in social science. Standard assumptions about human behavior (e.g., rationality) imply restrictions on the plausible values of the causal effect. In addition to this effect, these restrictions may depend on additional summaries of human behavior. Estimation of these additional parameters presents a trade-off between capturing the complexity of human's decision-making yet constraining it to deliver precise estimates. I resolve this tension by incorporating modern machine learning tools into the estimation of the additional parameters and deliver high-quality estimates of the causal effect and counterfactual outcomes. I estimate the causal effect in a two-stage procedure. At the first stage, I estimate the additional summaries of human behavior by modern machine learning tools. At the second stage, I plug the first-stage output into the sample analog of the restriction that identifies the causal effect. I modify the second-stage restriction to make it insensitive to any regularization biases present in the first-stage components. The second-stage estimate of the causal effect is of high-quality: it converges at fastest rate and can be used to test the hypotheses and build the confidence intervals for the values of the causal effect. I apply this idea in a wide class of economic models, including dynamic games of imperfect information, treatment effect in the presence of endogenous sample selection, and reduced-form demand estimation. by Vira Semenova. Ph. D. Ph.D. Massachusetts Institute of Technology, Department of Economics 2019-10-11T22:11:07Z 2019-10-11T22:11:07Z 2018 2018 Thesis https://hdl.handle.net/1721.1/122542 1121629417 eng MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 213 pages application/pdf Massachusetts Institute of Technology
collection NDLTD
language English
format Others
sources NDLTD
topic Economics.
spellingShingle Economics.
Semenova, Vira.
Essays in econometrics and machine learning
description Thesis: Ph. D., Massachusetts Institute of Technology, Department of Economics, 2018 === Cataloged from PDF version of thesis. === Includes bibliographical references (pages 209-213). === Establishing the link between a cause and effect is a fundamental question in social science. Standard assumptions about human behavior (e.g., rationality) imply restrictions on the plausible values of the causal effect. In addition to this effect, these restrictions may depend on additional summaries of human behavior. Estimation of these additional parameters presents a trade-off between capturing the complexity of human's decision-making yet constraining it to deliver precise estimates. I resolve this tension by incorporating modern machine learning tools into the estimation of the additional parameters and deliver high-quality estimates of the causal effect and counterfactual outcomes. I estimate the causal effect in a two-stage procedure. At the first stage, I estimate the additional summaries of human behavior by modern machine learning tools. At the second stage, I plug the first-stage output into the sample analog of the restriction that identifies the causal effect. I modify the second-stage restriction to make it insensitive to any regularization biases present in the first-stage components. The second-stage estimate of the causal effect is of high-quality: it converges at fastest rate and can be used to test the hypotheses and build the confidence intervals for the values of the causal effect. I apply this idea in a wide class of economic models, including dynamic games of imperfect information, treatment effect in the presence of endogenous sample selection, and reduced-form demand estimation. === by Vira Semenova. === Ph. D. === Ph.D. Massachusetts Institute of Technology, Department of Economics
author2 Victor Chernozhukov, Whitney Newey and Anna Mikusheva.
author_facet Victor Chernozhukov, Whitney Newey and Anna Mikusheva.
Semenova, Vira.
author Semenova, Vira.
author_sort Semenova, Vira.
title Essays in econometrics and machine learning
title_short Essays in econometrics and machine learning
title_full Essays in econometrics and machine learning
title_fullStr Essays in econometrics and machine learning
title_full_unstemmed Essays in econometrics and machine learning
title_sort essays in econometrics and machine learning
publisher Massachusetts Institute of Technology
publishDate 2019
url https://hdl.handle.net/1721.1/122542
work_keys_str_mv AT semenovavira essaysineconometricsandmachinelearning
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