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This dissertation provides innovative procedures for the estimation of the nonparametric triangular simultaneous equations model, extending beyond the traditional independent and identically distributed (i.i.d.) error structures in the reduced form and exploring its applications in empirical research.Chapter 1 provides a comprehensive introduction to nonparametric simultaneous equations models, reviewing the various estimation methodologies discussed in the existing literature and limitation.Chapter 2 advances the nonparametric estimation of structural equations by relaxing the i.i.d. error assumption in reduced form. I introduces a pre-whitening process designed to address heteroscedasticity in the nonparametric estimation of structural equations, followed by simulation analysis and asymptotic results.Chapter 3 develops two novel methods for estimating structural equations under a nonparametric AR(1) error structure, supported by extensive simulation studies. The result demonstrates the consistency and efficiency of the proposed estimators, effectively managing both endogeneity and autocorrelation within the nonparametric estimation framework.Chapter 4 highlights the practical application of the proposed estimators through an empirical analysis of cross-border air pollution spillover effects in the East Asian region. Using daily data from the recent COVID-19 period, this chapter employs a fully nonparametric approach to rigorously quantify and analyze the sources and magnitudes of air pollution and spillover effects. Additionally, I analyze the economic implications and policy recommendations derived from the findings.

We investigate how prior-biased inferences change players' strategic incentives and result in novel welfare implications in the canonical framework of strategic information transmission. The ex ante social welfare achieved in our model exceeds the upper bound characterized in the standard environment without prior bias. The welfare gain stems from the fact that the receiver's prior bias weakens the link between the sender's message and the receiver's response without contaminating the actual content of the messages. We further show that direct communication is optimal among all possible communication protocols in the presence of a sufficient degree of prior bias. © 2023 Elsevier Inc.

As text-to-image (T2I) synthesis models increase in size, they demand higher inference costs due to the need for more expensive GPUs with larger memory, which makes it challenging to reproduce these models in addition to the restricted access to training datasets. Our study aims to reduce these inference costs and explores how far the generative capabilities of T2I models can be extended using only publicly available datasets and open-source models. To this end, by using the de facto standard text-to-image model, Stable Diffusion XL (SDXL), we present three key practices in building an efficient T2I model: (1) Knowledge distillation: we explore how to effectively distill the generation capability of SDXL into an efficient U-Net and find that self-attention is the most crucial part. (2) Data: despite fewer samples, high-resolution images with rich captions are more crucial than a larger number of low-resolution images with short captions. (3) Teacher: Step-distilled Teacher allows T2I models to reduce the noising steps. Based on these findings, we build two types of efficient text-to-image models, called KOALA-Turbo &-Lightning, with two compact U-Nets (1B & 700M), reducing the model size up to 54% and 69% of the SDXL U-Net. In particular, the KOALA-Lightning-700M is 4x faster than SDXL while still maintaining satisfactory generation quality. Moreover, unlike SDXL, our KOALA models can generate 1024px high-resolution images on consumer-grade GPUs with 8GB of VRAMs (3060Ti). We believe that our KOALA models will have a significant practical impact, serving as cost-effective alternatives to SDXL for academic researchers and general users in resource-constrained environments., Comment: Project page: https://youngwanlee.github.io/KOALA

Aerial image registration or matching is a geometric process of aligning two aerial images captured in different environments. Estimating the precise transformation parameters is hindered by various environments such as time, weather, and viewpoints. The characteristics of the aerial images are mainly composed of a straight line owing to building and road. Therefore, the straight lines are distorted when estimating homography parameters directly between two images. In this paper, we propose a deep homography alignment network to precisely match two aerial images by progressively estimating the various transformation parameters. The proposed network is possible to train the matching network with a higher degree of freedom by progressively analyzing the transformation parameters. The precision matching performances have been increased by applying homography transformation. In addition, we introduce a method that can effectively learn the difficult-to-learn homography estimation network. Since there is no published learning data for aerial image registration, in this paper, a pair of images to which random homography transformation is applied within a certain range is used for learning. Hence, we could confirm that the deep homography alignment network shows high precision matching performance compared with conventional works.

The fundamental question I address in the dissertation is how the behavior of economic agents interacts with networks of relationships which underlie a wide set of economic situations. In Ch. 2, entitled "Decentralized Information Sharing in Oligopoly," I analyze the incentives of firms for information sharing in a decentralized environment when firms face a stochastic demand. In order to do that, I develop a two stage model of strategic network formation, where a cooperative network formation stage is played in the first stage and a noncooperative Bayesian Cournot game is played in the second stage. I derive pure strategy mixed cooperative and noncooperative equilibria that are subgame perfect and stable, and characterize the resulting network structures. Ch. 3, entitled "A War of Attrition in Network Formation," investigates the strategic behavior of agents when they face a decision on the formation of relationships. I apply a war of attrition to the dynamic network formation process when links among agents have characteristics of public goods. Agents are randomly but exogenously matched in each stage. Based on Bala and Goyal's (2000) two-way flow model, I characterize the subgame-perfect equilibrium outcomes and discuss their efficiency. Finally, Ch. 4, entitled "Social Norms and Trust among Strangers," (with Huan Xie) studies the development of trust and reciprocity among strangers in the indefinitely repeated trust game with random matching. If reputation is attached to the community as a whole and if a single defection leads to the destruction of the cooperative social norm through contagious punishments, the cooperative social norm can be sustained by the self-interested community members in the equilibrium. We provide sufficient conditions that support the social norm of trust and reciprocity as a sequential equilibrium.

The fundamental question I address in the dissertation is how the behavior of economic agents interacts with networks of relationships which underlie a wide set of economic situations. In Ch. 2, entitled "Decentralized Information Sharing in Oligopoly," I analyze the incentives of firms for information sharing in a decentralized environment when firms face a stochastic demand. In order to do that, I develop a two stage model of strategic network formation, where a cooperative network formation stage is played in the first stage and a noncooperative Bayesian Cournot game is played in the second stage. I derive pure strategy mixed cooperative and noncooperative equilibria that are subgame perfect and stable, and characterize the resulting network structures. Ch. 3, entitled "A War of Attrition in Network Formation," investigates the strategic behavior of agents when they face a decision on the formation of relationships. I apply a war of attrition to the dynamic network formation process when links among agents have characteristics of public goods. Agents are randomly but exogenously matched in each stage. Based on Bala and Goyal's (2000) two-way flow model, I characterize the subgame-perfect equilibrium outcomes and discuss their efficiency. Finally, Ch. 4, entitled "Social Norms and Trust among Strangers," (with Huan Xie) studies the development of trust and reciprocity among strangers in the indefinitely repeated trust game with random matching. If reputation is attached to the community as a whole and if a single defection leads to the destruction of the cooperative social norm through contagious punishments, the cooperative social norm can be sustained by the self-interested community members in the equilibrium. We provide sufficient conditions that support the social norm of trust and reciprocity as a sequential equilibrium.