자료유형  

 학위논문

Control Number  

0017161060

International Standard Book Number  

9798382741741

Dewey Decimal Classification Number  

310

Main Entry-Personal Name  

Chang, Tae Hun.

Publication, Distribution, etc. (Imprint  

[S.l.] : Georgetown University., 2024

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2024

Physical Description  

112 p.

General Note  

Source: Dissertations Abstracts International, Volume: 85-11, Section: B.

General Note  

Advisor: Komunjer, Ivana.

Dissertation Note  

Thesis (Ph.D.)--Georgetown University, 2024.

Summary, Etc.  

요약In the dissertation, I analyze both empirical and theoretical topics related to the existence of latent variables. In Chapter 1, I propose a non-linear time-series model incorporating a particular neural network algorithm, a recurrent neural network. The construction of neural network algorithms shows the models approximate the data process by taking advantage of latent hidden units. A proposed model combines an economic model-based restriction with the recurrent neural network algorithm, where serial correlation is considered and the results are interpretable in terms of economics. I investigate the inflation dynamics using the model and estimate the instability of the Phillips curve after COVID-19. The model can also forecast atypical changes in inflation rates since COVID-19. In Chapter 2, co-authored with Ivana Komunjer, we propose a state space model for panel data, which is useful to analyze the non-constant influences of latent characteristics on economic outcome variables without proxy variables. We show the similarity transform of model parameters and identify the model parameters from the first and second moments of the data. We consider two specific situations: an assumption for the initial condition of a latent characteristic, and an assumption for the conditional variance of idiosyncratic shock. We obtain rank conditions for local identification from the similarity transformation relations of model parameters. Finally, in Chapter 3, co-authored with Youngjin Lee, we investigate the effect of expectations on house prices. Expectations on the future are crucial determinants of house prices, and many surveyed measures elicit these expectations. However, estimating the effect of the latent characteristic is difficult because various surveys exist, and the results depend on the type of survey used. We use dynamic factor models to derive proper indices for expectations from multiple measures, and we show how expectations have an important influence on house prices.

Subject Added Entry-Topical Term  

Statistics.

Index Term-Uncontrolled  

Econometrics

Index Term-Uncontrolled  

Latent characteristics

Index Term-Uncontrolled  

Time-varying parameters

Index Term-Uncontrolled  

Recurrent neural networks

Index Term-Uncontrolled  

House prices

Added Entry-Corporate Name  

Georgetown University Economics

Host Item Entry  

Dissertations Abstracts International. 85-11B.

Electronic Location and Access  

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Control Number  

joongbu:655865