Bayesian Multilevel Analysis of Binary Time-Series Cross-Sectional Data in Political Economy

In this dissertation project, I propose a Bayesian generalized linear multilevel model with pth order autoregressive errors: GLMM-AR(p)) for modeling inter-temporal dependence, con-temporary correlation, and heterogeneity of unbalanced binary Time- Series Cross-Sectional data. The model includes two unnested sources of clustering in the unit- and time-dimensions for analyzing heterogeneities and contemporal corre- lation which are salient in the era of globalization. Group-level variations are further explained with unit- and time-specific characteristics. For handling dynamics in pol- itics and political economy, I apply the autoregressive error specification to analyze serial correlation which may not be fully captured by the selected covariates. Two applications on civil war and sovereign default demonstrate how the proposed model controls for multiple potential confounders. It also improves reliability of statistical inferences and helps forecasts by more efficiently using the information in data. The first application focuses on the causal relationship between ethnic minority rule and civil war onset. The GLMM-AR(p) model helps study those background factors which affect the relationship under investigation. The second applied study considers how regime duration affects sovereign default conditional on regime type by putting the national policy-making regarding repaying external debt into the international context. To model the heterogeneous vulnerability or sensitivity of the developing countries to global shocks, I extend the GLMM-AR(p) model to analyze time-specific unit-varying effects

Continuous-time Mean-Variance Portfolio Selection with Stochastic Parameters

This paper studies a continuous-time market {under stochastic environment} where an agent, having specified an investment horizon and a target terminal mean return, seeks to minimize the variance of the return with multiple stocks and a bond. In the considered model firstly proposed by [3], the mean returns of individual assets are explicitly affected by underlying Gaussian economic factors. Using past and present information of the asset prices, a partial-information stochastic optimal control problem with random coefficients is formulated. Here, the partial information is due to the fact that the economic factors can not be directly observed. Via dynamic programming theory, the optimal portfolio strategy can be constructed by solving a deterministic forward Riccati-type ordinary differential equation and two linear deterministic backward ordinary differential equations

Ruminal microbiota and muscle metabolome characteristics of Tibetan plateau yaks fed different dietary protein levels

IntroductionThe dietary protein level plays a crucial role in maintaining the equilibrium of rumen microbiota in yaks. To explore the association between dietary protein levels, rumen microbiota, and muscle metabolites, we examined the rumen microbiome and muscle metabolome characteristics in yaks subjected to varying dietary protein levels.MethodsIn this study, 36 yaks were randomly assigned to three groups (n = 12 per group): low dietary protein group (LP, 12% protein concentration), medium dietary protein group (MP, 14% protein concentration), and high dietary protein group (HP, 16% protein concentration).Results16S rDNA sequencing revealed that the HP group exhibited the highest Chao1 and Observed_species indices, while the LP group demonstrated the lowest. Shannon and Simpson indices were significantly elevated in the MP group relative to the LP group (P < 0.05). At the genus level, the relative abundance of Christensenellaceae_R-7_group in the HP group was notably greater than that in the LP and MP groups (P < 0.05). Conversely, the relative abundance of Rikenellaceae_RC9_gut_group displayed an increasing tendency with escalating feed protein levels. Muscle metabolism analysis revealed that the content of the metabolite Uric acid was significantly higher in the LP group compared to the MP group (P < 0.05). The content of the metabolite L-(+)-Arabinose was significantly increased in the MP group compared to the HP group (P < 0.05), while the content of D-(-)-Glutamine and L-arginine was significantly reduced in the LP group (P < 0.05). The levels of metabolites 13-HPODE, Decanoylcarnitine, Lauric acid, L-(+)-Arabinose, and Uric acid were significantly elevated in the LP group relative to the HP group (P < 0.05). Furthermore, our observations disclosed correlations between rumen microbes and muscle metabolites. The relative abundance of NK4A214_group was negatively correlated with Orlistat concentration; the relative abundance of Christensenellaceae_R-7_group was positively correlated with D-(-)-Glutamine and L-arginine concentrations.DiscussionOur findings offer a foundation for comprehending the rumen microbiome of yaks subjected to different dietary protein levels and the intimately associated metabolic pathways of the yak muscle metabolome. Elucidating the rumen microbiome and muscle metabolome of yaks may facilitate the determination of dietary protein levels

Chemokine receptor CXCR3 is important for lung tissue damage and airway remodeling induced by short-term exposure to cigarette smoking in mice

Aim: To investigate the role of chemokine receptor CXCR3 in cigarette smoking (CS)-induced pulmonary damage. Methods: CXCR3 knockout (CXCR3-/-) mice were used. Differences in airspace enlargement, mRNA expression of matrix metalloproteinases (MMPs), transforming growth factor (TGF) β1, CXCL10 in lung homogenates, and CXCL10 content in bronchoalveolar lavage (BAL) fluids and homogenates were compared between CXCR3-/- mice and wild-type (WT) mice three days after three-day CS exposures. Results: The linear intercept was significantly less in CXCR3-/- mice than in WT mice (30.1±0.9 μm vs 40.3±2.4 μm, P<0.01). Morphologically, collagen was deposited less around airways and vessels in CXCR3-/- mice. The lung hydroxyproline content was significantly lower in CXCR3-/- mice than in WT mice (6.0±1.0 μg/mL vs 12.0±1.6 μg/mL, P<0.05). Profoundly lower mRNA expression of MMP2, MMP12, TGFβ1, and CXCL10 was seen in lung homogenates from CXCR3-/- mice. CXCL10 concentrations in BAL fluid and lung homogenates were significantly lower in CXCR3-/- mice than in WT mice (BAL fluid: 19.3±1.4 pg/mL vs 24.8±1.6 pg/mL, P<0.05; lung homogenates: 76.6±7.0 pg/mL vs 119.5±15.9 pg/mL, P<0.05). Conclusion: CXCR3 is important in mediating lung tissue damage and airway remodeling following a short-term CS insult, possibly through up-regulation of CXCL10 and inducement of mRNA expression of MMPs. Targeting CXCR3 may be helpful for prevention of CS-induced pulmonary pathology

Endogenous Jurisprudential Regimes

Jurisprudential regime theory is a legal explanation of decision-making on the U.S. Supreme Court that asserts that a key precedent in an area of law fundamentally restructures the relationship between case characteristics and the outcomes of future cases. In this article, we offer a multivariate multiple change-point probit model that can be used to endogenously test for the existence of jurisprudential regimes. Unlike the previously employed methods, our model does so by estimating the locations of many possible changepoints along with structural parameters. We estimate the model using Markov chain Monte Carlo methods, and use Bayesian model comparison to determine the number of change-points. Our findings are consistent with jurisprudential regimes in the Establishment Clause and administrative law contexts. We find little support for hypothesized regimes in the areas of free speech and search-and-seizure. The Bayesian multivariate change-point model we propose has broad potential applications to studying structural breaks in either regular or irregular time-series data about political institutions or processes.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/116095/1/pa12.pd

When silence is golden: The use of strategic silence in crisis management

Presented at Conference on Corporate Communication 2018, May 29 – June 1, New York</p