Three Essays on Commodity Markets and Health Economics

This dissertation includes three essays. The first essay examines time-varying nonlinear dependence and asymmetries of commodity futures from 1999 to 2015. We consider several elliptical copulas with dynamic conditional correlation (DCC) and block dynamic equicorrelation (Block DECO) to capture dependence structure of various commodities across different sectors. Our major findings include: (1) flexible copula specification that allows for multivariate asymmetry and tail dependence appears to have the best model performance in characterizing co-movements of commodity returns. (2) dynamic correlations reveal connectedness degree between commodities has dramatically increased during the financial distress and the European debt crisis, but they declined sharply after 2012 and returned to the precrisis level since. (3) conditional diversification benefit is disappearing and lower tail dependence between commodity markets is much higher in the bearish market. The second essay studies volatility spillover and various connectedness measures for 20 commodity futures from 1996 to 2016. We propose to estimate network connectedness in commodity markets by a previous framework that models direction and magnitude of volatility spillover using reduced-form vector autoregression (VAR) models and generalized forecast error variance decomposition. We find clustering of commodity futures that match their industrial groupings, and energy markets have played a central role in the network in the static analysis. Our dynamic models show that though market interconnections have dramatically increased during the 2007-2009 financial crisis, they have returned to the pre-crisis levels after. We also find that recent downward movement of crude oil prices does not necessarily lead to stronger connectedness between commodity markets. The third essay investigates health economics in developing countries. Obesity and overweight problems have become prevalent in developing countries like China. This paper presents a comprehensive analysis on body mass index (BMI) using a micro-level data of Chinese families. We model the dynamics of BMI determinants spanning from 1991 to 2011 for rural and urban residents. Our identification strategies include: (1) using spousal and parental characteristics as proxy variables to control for omitted variables bias and (2) explicitly modeling common couple effect with correlated random-effects regressions for spousal BMI. Our results find strong and positive spousal/intergenerational transmissions of BMI for families across region and time. Depending on the gender of spouse and grown children, besides transmission effects a variety of socioeconomic variables are identified as significant predictors of individual BMI

Risk Perception and Willingness to Pay for Removing Arsenic in Drinking Water

This thesis is concerned with (i) how to estimate the perceived mortality risk, (ii) how to calculate the welfare change of mortality risk reduction and (iii) whether ambiguity aversion influences subjects' treatment decision. This study is an important topic in environmental and resource economics, and the attempt to introduce ambiguity preference into the models might shed light on future research in nonmarket valuation. In this study, I estimate the economic value of reducing mortality risk relating to arsenic in drinking water employing contingent valuation in U.S. arsenic hot spots. Re-cent studies have shown that perceived risk is a more reliable variable than scientific assessments of risk when applied to interpret and predict individual's averting behavior. I am also interested in the confidence level of perceived risk, which was elicited and treated as the degree of risk ambiguity in this paper. I develop a formal parametric model to calculate the mean willingness to pay (WTP) for mortality risk reduction, and find weak evidence of ambiguity aversion

Periodic Heat Shock Accelerated the Chondrogenic Differentiation of Human Mesenchymal Stem Cells in Pellet Culture

Osteoarthritis (OA) is one of diseases that seriously affect elderly people\u27s quality of life. Human mesenchymal stem cells (hMSCs) offer a potential promise for the joint repair in OA patients. However, chondrogenic differentiation from hMSCs in vitro takes a long time (∼6 weeks) and differentiated cells are still not as functionally mature as primary isolated chondrocytes, though chemical stimulations and mechanical loading have been intensively studied to enhance the hMSC differentiation. On the other hand, thermal stimulations of hMSC chondrogenesis have not been well explored. In this study, the direct effects of mild heat shock (HS) on the differentiation of hMSCs into chondrocytes in 3D pellet culture were investigated. Periodic HS at 41°C for 1 hr significantly increased sulfated glycosaminoglycan in 3D pellet culture at Day 10 of chondrogenesis. Immunohistochemical and Western Blot analyses revealed an increased expression of collagen type II and aggrecan in heat-shocked pellets than non heat-shocked pellets on Day 17 of chondrogenesis. In addition, HS also upregulated the expression of collagen type I and X as well as heat shock protein 70 on Day 17 and 24 of differentiation. These results demonstrate that HS accelerated the chondrogenic differentiation of hMSCs and induced an early maturation of chondrocytes differentiated from hMSCs. The results of this study will guide the design of future protocols using thermal treatments to facilitate cartilage regeneration with human mesenchymal stem cells

Robust Ranking Explanations

Robust explanations of machine learning models are critical to establish human trust in the models. Due to limited cognition capability, most humans can only interpret the top few salient features. It is critical to make top salient features robust to adversarial attacks, especially those against the more vulnerable gradient-based explanations. Existing defense measures robustness using $\ell_p$-norms, which have weaker protection power. We define explanation thickness for measuring salient features ranking stability, and derive tractable surrogate bounds of the thickness to design the \textit{R2ET} algorithm to efficiently maximize the thickness and anchor top salient features. Theoretically, we prove a connection between R2ET and adversarial training. Experiments with a wide spectrum of network architectures and data modalities, including brain networks, demonstrate that R2ET attains higher explanation robustness under stealthy attacks while retaining accuracy.Comment: Accepted to IMLH (Interpretable ML in Healthcare) workshop at ICML 2023. arXiv admin note: substantial text overlap with arXiv:2212.1410