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Title: Essays on Machine Learning Methods and Household Dynamics
Authors: Sorg-Langhans, George Leopold
Advisors: Rogerson, Richard
Contributors: Economics Department
Keywords: Consumption Insurance
Household Dynamics
Machine Learning
Subjects: Economics
Issue Date: 2021
Publisher: Princeton, NJ : Princeton University
Abstract: This dissertation consists of three independent chapters on questions surrounding household dynamics and machine learning methods developed to address them. In the first chapter, which is co-authored with Jesus Fernandez-Villaverde, Galo Nuno, and Maximilian Vogler, we develop a deep-learning algorithm to globally solve high-dimensional dynamic programming problems that arise in macroeconomic models. This approach allows us to address household dynamics in a rich environment driven by both aggregate and idiosyncratic uncertainty. We evaluate our methodology in a standard neoclassical growth model and then demonstrate its power in two high-dimensional applications -- a model of dynamic capital allocation and a model of migration and labor mobility. In the second chapter I propose a new machine learning approach to understanding consumption insurance of households, a central issue in the context of household dynamics. I draw on a state-of-the-art machine learning method, gradient boosted trees, to predict consumption in the Panel Study of Income Dynamics. With the resulting panel data set in hand, I adopt Blundell, Pistaferri, and Preston's (2008) assumptions about the underlying permanent-transitory income process, which allows me to estimate insurance coefficients. Importantly, I find qualitative and quantitative differences to their insurance predictions. In the third chapter, which is co-authored with Riccardo Cioffi, and Maximilian Vogler, we investigate how different theories of wealth inequality interact with important policy experiments. To this end, we calibrate four models, each incorporating a different inequality-generating channel emphasized in the theoretical literature, and compare their predictions regarding different policy experiments. We find stark quantitative and qualitative differences in predictions across channels for a given policy experiment, indicating that analyzing their relative importance is crucial to our understanding of wealth inequality.
Alternate format: The Mudd Manuscript Library retains one bound copy of each dissertation. Search for these copies in the library's main catalog:
Type of Material: Academic dissertations (Ph.D.)
Language: en
Appears in Collections:Economics

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