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Statistical Learning Applications in...

McBride, Linden E.

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Statistical Learning Applications in Development Economics.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Statistical Learning Applications in Development Economics./
作者:	McBride, Linden E.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
面頁冊數:	168 p.
附註:	Source: Dissertations Abstracts International, Volume: 80-03, Section: A.
Contained By:	Dissertations Abstracts International80-03A.
標題:	Economics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10845866
ISBN:	9780438343719

Statistical Learning Applications in Development Economics.
McBride, Linden E.

Statistical Learning Applications in Development Economics. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 168 p.

Source: Dissertations Abstracts International, Volume: 80-03, Section: A.

Thesis (Ph.D.)--Cornell University, 2018.

This item must not be added to any third party search indexes.

The focus of this dissertation is the application of statistical learning and computational thinking to stubborn problems in development economics and welfare dynamics including the problems of poverty targeting, the identification of heterogeneous welfare dynamics, and the assessment of the risks and returns of diverse asset portfolios. Approaching such problems with statistical learning has allowed me to overcome some of the commonly imposed constraints and weaken some of the commonly made assumptions that prevent us from learning more about the empirical problem. By using out-of-sample validation and algorithmic model building, the first chapter presents an important step forward in making poverty targeting more accurate and efficient. The second chapter considers the theory and empirics of poverty and welfare dynamics more generally; the findings include several important implications for the study of welfare dynamics in diverse asset environments. The final chapter finds evidence consistent with a pattern in which households with greater initial asset holdings also hold a riskier portfolios and enjoy greater returns to their assets; however the analysis is limited by a poor accounting of human capital assets. The chapter concludes that allowing for the heterogeneity of assets, including non-physical assets, that may play a role in the livelihoods of households in developing countries is important. This dissertation demonstrates some of the ways in which algorithmic approaches can assist us in learning from the data.

ISBN: 9780438343719Subjects--Topical Terms:

517137
Economics.

Statistical Learning Applications in Development Economics.
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520 $a The focus of this dissertation is the application of statistical learning and computational thinking to stubborn problems in development economics and welfare dynamics including the problems of poverty targeting, the identification of heterogeneous welfare dynamics, and the assessment of the risks and returns of diverse asset portfolios. Approaching such problems with statistical learning has allowed me to overcome some of the commonly imposed constraints and weaken some of the commonly made assumptions that prevent us from learning more about the empirical problem. By using out-of-sample validation and algorithmic model building, the first chapter presents an important step forward in making poverty targeting more accurate and efficient. The second chapter considers the theory and empirics of poverty and welfare dynamics more generally; the findings include several important implications for the study of welfare dynamics in diverse asset environments. The final chapter finds evidence consistent with a pattern in which households with greater initial asset holdings also hold a riskier portfolios and enjoy greater returns to their assets; however the analysis is limited by a poor accounting of human capital assets. The chapter concludes that allowing for the heterogeneity of assets, including non-physical assets, that may play a role in the livelihoods of households in developing countries is important. This dissertation demonstrates some of the ways in which algorithmic approaches can assist us in learning from the data.
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