A Bayesian Model of Sample Selection with a Discrete Outcome Variable

Relatively few published studies apply Heckman’s (1979) sample selection model to the case of a discrete endogenous variable and those are limited to a single outcome equation. However, there are potentially many applications for this model in health, labor and financial economics. To fill in this theoretical gap, I extend the Bayesian multivariate probit setup of Chib and Greenberg (1998) into a model of non-ignorable selection that can handle multiple selection and discrete-continuous outcome equations. The first extension of the multivariate probit model in Chib and Greenberg (1998) allows some of the outcomes to be missing. In addition, I use Cholesky factorization of the variance matrix to avoid the Metropolis-Hastings algorithm in the Gibbs sampler. Finally, using artificial data I show that the model is capable of retrieving the parameters used in the data-generating process and also that the resulting Markov Chain passes all standard convergence tests

Essays on Systemic Risk: An analysis from multiple perspectives

This thesis is about systemic risk in the financial sector. It considers several aspects of systemic risk. It is a building block for an analysis of the impact of systemic risk on the real economy. It appears that stocks in the financial industry show a strong interdependence compared to stocks in other industries. This applies to both European and US equities. The strong interdependence suggests a major systemic risk for financials. At the same time, it is generally accepted that regulators will implicitly or explicitly step in to prevent a systemic crisis in the financial industry. Markets anticipate this warranty by accepting a lower return on financial stocks

Implementation research for integrated health system strengthening in Ghana : towards tipping point for improved health systems performance and population health

Recent decades have witnessed the proliferation of large-scale initiatives for improving health systems. Strategies such as the Bamako Initiative, the Sector-Wide Approach, Child Survival+ and many others were instituted with compelling rationales for improving the provision of essential health services. Yet, large-scale investments in untested health system initiatives have often been associated with disappointing results, or with little formal evidence that investments in organizational strategies have actually improved health. Interestingly, no prior study has tested the proposition that the six WHO health system building block subsystems (integrated health service delivery, health workforce, information for decision making, essential drug supplies and logistics, health financing and resources allocation and leadership and governance) can be strengthened with an integrated package of systems interventions in ways that can accelerate the pace of improvements in child health and survival. If such incremental effects can be demonstrated, prospects for expanding international and national commitment to health systems strengthening will be greatly enhanced and specific lessons from implementation research and operational experience of this nature will be invaluable to health planners. Health services delivery in Ghana is decentralized and in discharging its constitutional mandate to expand access to healthcare, the Government of Ghana has implemented policies that mandate a system of services, referral operations and supervisory roles for health care services that is provided in hospitals, sub-district health centres and community-based facilities. Health service innovations are provided at the community level through a policy known as the Community-based Health Planning and Services (CHPS) Initiative that aims to mobilize community leadership, decision-making systems and resources in poor rural areas; relocate facility-based nurses to community service points called “CHPS zones” and orient these workers to the active provision of community-based outreach and doorstep healthcare. CHPS also supports nurses with logistics and community volunteer systems to provide services according to the principles of primary healthcare including integrated management of childhood illnesses, comprehensive immunization services and basic safe motherhood care. Despite efforts to implement this community-based health system, a number of challenges have emerged that limit access to service delivery using the six WHO health systems building block subsystems. Critically identified are the following challenges: 1. Governance: Leadership and governance systems are poorly understood and inadequately marshalled for health development at the local and community level. 2. Financing: Budgets and financial plans are largely determined by past budgets or external vertical programmes rather than resource allocation that is based on the configuration of need. 3. Information: Health information capability to support decentralized healthcare system has instead been a time consuming data extraction process for the health insurance and central health bureaucracy rather than a system for community-based healthcare workers that contributes to their work, decision-making, or supervisory support needs. 4. Logistics: Even though there is deemed to be a decentralized management of health services, there is still a centralized medical stores system, resulting in episodes of catastrophic breakdown in supply chains, with stock-outs that are exacerbated when district health service operations increase. 5. Human resources: Shortages in the district health management, midwifery, and nursing workforce arise, either because of their inappropriate posting location or inadequate numbers as well as poor leadership that seriously undermines efforts to strengthen the health systems. This work reviews the Ghana Essential Health Interventions Project (GEHIP), implemented in the Upper East Region of Ghana to contribute to the health systems strengthening policy by testing the health and survival impact of strengthening the primary health care system. GEHIP tested the hypothesis that integrated system initiatives cutting across the WHO “pillars” of health system strengthening can improve system performance to the point of having an impact on population and health outcomes and ensure that essential health interventions reach under-served populations and progress towards Millennium Development Goal (MDG) 4 can be achieved. The project essentially focused on strengthening district-level capacity to plan and set priorities using locally obtained burden of disease and cost-effectiveness data in order to increase the effectiveness of Ghana’s Community-based Health Planning and Services (CHPS) programme, with the goal of accelerating the expansion of CHPS coverage and improving the quality of CHPS provided care. A mixed methodology was used to gauge the impact of the health system functioning according to a framework of interventions spanning the six WHO health systems building block subsystems. Aggregate impact of GEHIP on child survival was tested with the Heckman “difference of differences” procedure using results from a baseline survey that was conducted in 2010 and an endline survey conducted in 2015 in four treatment and seven comparison districts. Qualitative Systems Appraisal (QSA) in the form of in-depth interviews and focus group investigations of community stakeholders, frontline workers, supervisors, and district health managers was employed to gauge reactions to the GEHIP system, clarify inputs by the health subsystem, reactions to these inputs and recommendations for systems change. Regression methods were used to refine the Heckman procedure, adjusting for potential confounders and estimating net effects of household exposure to GEHIP improved care on the survival of children. GEHIP is a quasi-experimental study of a project designed to accelerate the scale up of one of the most effective health development experiments ever conducted in Africa –The “Navrongo Experiment”. It supplements the provision of effective primary healthcare strategies with leadership training, field demonstration, improved budgeting and resource mobilization. By means of these interventions, GEHIP sought to enhance health equity, mitigate social and monetary health care costs, foster parental health seeking behaviour and improve maternal and child survival. Training was designed to expand access to life saving technology that reduces neonatal, infant, and childhood mortality. Additional components for improving referral, neonatal survival, and maternal health rekindled Ghana’s legacy of generating evidence-based means of achieving affordable and accessible primary health care throughout Ghana. Findings from this work have shown that the combined effects of leadership training, catalytic investment, political engagement, and evidence-based budgeting are capable of solving CHPS start-up problem and accelerate scale up. At baseline, neonatal and maternal mortality rates were unacceptably high, but the rapid training of frontline workers proved to be inexpensive, operationally feasible, and potentially effective in reducing maternal and neonatal mortality. Moreover, an innovative pilot referral system utilizing locally appropriate tri-car ambulances has been implemented and information systems have been reformed through the adoption of a simplified register system with impressive results. Accelerating CHPS scale-up is crucial to health development in Ghana where the expansion of CHPS has languished because district health systems strengthening requirements were unanticipated by national policies. Research results showed that the interventions had their intended impact on the pace of CHPS scale-up. This success translated into an impact on child mortality resulting in GEHIP providing a critically needed focus for national efforts to develop primary health care, and lessons for global healt

Graphical Models: Modeling, Optimization, and Hilbert Space Embedding

Over the past two decades graphical models have been widely used as a powerful tool for compactly representing distributions. On the other hand, kernel methods have also been used extensively to come up with rich representations. This thesis aims to combine graphical models with kernels to produce compact models with rich representational abilities. The following four areas are our focus. 1. Conditional random fields for multi-agent reinforcement learning. Conditional random fields (CRFs) are graphical models for modeling the probability of labels given the observations. They have traditionally assumed that, conditioned on the training data, the label sequences of different training examples are independent and identically distributed (iid). We extended the use of CRFs to a class of temporal learning algorithms, namely policy gradient reinforcement learning (RL). Now the labels are no longer iid. They are actions that update the environment and affect the next observation. From an RL point of view, CRFs provide a natural way to model joint actions in a decentralized Markov decision process. Using tree sampling for inference, our experiment shows the RL methods employing CRFs clearly outperform those which do not model the proper joint policy. 2. Bayesian online multi-label classification. Gaussian density filtering provides fast and effective inference for graphical models (Maybeck, 1982). Based on it, we propose a Bayesian online multi-label classification (BOMC) framework which learns a probabilistic model of the linear classifier. The training labels are incorporated to update the posterior of the classifiers via a graphical model similar to TrueSkill (Herbrich et al, 2007). Using samples from the posterior, we label the test data by maximizing the expected F1-score. In our experiments, BOMC delivers significantly higher macro-averaged F1-score than the state-of-the-art online maximum margin learners. 3. Hilbert space embedment of distributions. Graphical models are also an essential tool in kernel measures of independence for non-iid data. Traditional information theory often requires density estimation, which makes it unideal for statistical estimation. Motivated by the fact that distributions often appear in machine learning via expectations, we can characterize the distance between distributions in terms of distances between means, especially means in reproducing kernel Hilbert spaces which are called kernel embeddings. Under this framework, the undirected graphical models further allow us to factorize the kernel embeddings onto cliques, which yields efficient measures of independence for non-iid data (Zhang et al, 2009). 4. Optimization in maximum margin models for structured data. Maximum margin estimation for structured data is an important task where graphical models also play a key role. They are special cases of regularized risk minimization, for which bundle methods (BMRM, Teo et al, 2007) are a state-of-the-art general purpose solver. Smola et al (2007) proved that BMRM requires O(1/epsilon) iterations to converge to an epsilon accurate solution, and we further show that this rate hits the lower bound. Motivated by (Nesterov 2003, 2005), we utilized the composite structure of the objective function and devised an algorithm for the structured loss which converges to an epsilon accurate solution in O(1/sqrt{epsilon}) iterations