Financial Development, Fiscal Policy and Macroeconomic Volatility

This thesis examines empirically the effect of financial frictions and public debt on economic variables and seeks for an appropriate fiscal consolidation strategy. First, the thesis explores the determinants of output volatility, especially the roles of financial development and government debt. The analysis, based on a panel of 127 countries over four decades, employs system GMM dynamic panel regression. According to the regression results financial development is estimated to have a non-linear effect on output volatility. Increased government debt levels are statistically associated with increased macroeconomic volatility. However, we need to interpret the results carefully due to endogeneity problems. The effect of the interactions between the two is insignificant. Second, it analyses the role of financial frictions on economic fluctuations. When the three models are compared with the U.S. data along the second moments, the firm friction model helps in fitting some macroeconomic variables and outperforms the other models. In the impulse response functions, we find that financial frictions greatly amplify and propagate the effects of the exogenous shocks on economic variables. Specially, the firm friction model shows more persistent response than the bank friction model. In addition, the size of the response depends on the leverage in the model with financial frictions. Third, the thesis considers how the effects of fiscal policy consolidation differ depending on alternative strategies. To do this, we develop an open economy DSGE model with an endogenous risk premium mechanism. The government consumption cut has larger negative effects on output than the government investment cut because of a complementarity with private consumption. The response of the tax hike is smaller than the expenditue cut because the tax hikes reduce more debts and the lower risk premium crowds in consumption and investment. Among three fiscal rules, the expenditure adjusted rule is the most effective for both preventing the economic downturn and reducing government debt

Finding pathway regulators: gene set approach using peak identification algorithms

Recently, a number of different approaches have been used to examine variation in gene expression and to identify genes whose level of transcript differed greatly among unrelated individuals. Previous studies have commonly focused on identifying determinants that regulate gene expressions by targeting individual genes. However, it is difficult to detect true differences in the level of gene expression among genotypes from noise due to issues such as multiple testing and limited sample size. To increase the statistical power for detecting this difference, we consider a 'gene set' approach by focusing on subtle but coordinated changes in gene expression across multiple genes rather than individual genes. We defined a 'gene set' as a set of genes in the same biological pathway and focused on identifying common regulators based on an assumption that the genes within the same pathway are controlled by common regulators. We applied the gene set approach to the expression data of mRNA in Centre d'Etude du Polymorphisme Humain lymphoblast cells to identify regulators controlling the genes in a biological pathway. Our gene set approach successfully identified potent regulators controlling gene expression in an inflammatory response pathway

Explaining generative diffusion models via visual analysis for interpretable decision-making process

Diffusion models have demonstrated remarkable performance in generation tasks. Nevertheless, explaining the diffusion process remains challenging due to it being a sequence of denoising noisy images that are difficult for experts to interpret. To address this issue, we propose the three research questions to interpret the diffusion process from the perspective of the visual concepts generated by the model and the region where the model attends in each time step. We devise tools for visualizing the diffusion process and answering the aforementioned research questions to render the diffusion process human-understandable. We show how the output is progressively generated in the diffusion process by explaining the level of denoising and highlighting relationships to foundational visual concepts at each time step through the results of experiments with various visual analyses using the tools. Throughout the training of the diffusion model, the model learns diverse visual concepts corresponding to each time-step, enabling the model to predict varying levels of visual concepts at different stages. We substantiate our tools using Area Under Cover (AUC) score, correlation quantification, and cross-attention mapping. Our findings provide insights into the diffusion process and pave the way for further research into explainable diffusion mechanisms.Comment: 22 pages, published in Expert Systems with Application