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dc.contributor.advisorIwata, Shigeru
dc.contributor.authorYin, Xunzhao
dc.date.accessioned2021-02-27T21:31:08Z
dc.date.available2021-02-27T21:31:08Z
dc.date.issued2019-12-31
dc.date.submitted2019
dc.identifier.otherhttp://dissertations.umi.com/ku:16902
dc.identifier.urihttp://hdl.handle.net/1808/31519
dc.description.abstractThe three essays included in this dissertation are on three di?erent popular computational approaches that are widely applicable in economics. In Chapter 1, a state-space model is constructed which is linear in state variables and nonlinear in parameters. From the model, the time-varying level of natural interest rate is estimated using Kalman ?lter and Gibbs sampling algorithms. Chapter 2 proposes a new algorithm, called Implicit Particle Gibbs, to solve nonlinear state-space models. And Chapter 3 reviews recent development of deep learning and reinforcement learning algorithms and their applications in economics.
dc.format.extent89 pages
dc.language.isoen
dc.publisherUniversity of Kansas
dc.rightsCopyright held by the author.
dc.subjectEconomics
dc.subjectParticle Filter
dc.subjectReinforcement Learning
dc.subjectr-Star
dc.subjectTerm Structure
dc.subjectYield Curve
dc.titleThree Essays on Computational Approaches to Economics
dc.typeDissertation
dc.contributor.cmtememberJalali, Azadeh
dc.contributor.cmtememberKeating, John
dc.contributor.cmtememberTu, Xuemin
dc.contributor.cmtememberZhang, Jianbo
dc.thesis.degreeDisciplineEconomics
dc.thesis.degreeLevelPh.D.
dc.identifier.orcid0000-0003-4637-2910
dc.rights.accessrightsopenAccess


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