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BackgroundFiscal constraints faced by U.S. hospitals as a result of the recent economic downturn are leading to business practices that reduce costs and improve financial and operational efficiency in hospitals. There naturally arises the question of how this finance-driven management culture could affect the quality of care. This paper attempts to determine whether the process measures of treatment quality are correlated with hospital financial performance.MethodsPanel study of hospital care quality and financial condition between 2005 and 2010 for cardiovascular disease treatment at acute care hospitals in the United States. Process measures for condition-specific treatment of heart attack and heart failure and hospital-level financial condition ratios were collected from the CMS databases of Hospital Compare and Cost Reports.ResultsThere is a statistically significant relationship between hospital financial performance and quality of care. Hospital profitability, financial leverage, asset liquidity, operating efficiency, and costs appear to be important factors of health care quality. In general, public hospitals provide lower quality care than their nonprofit counterparts, and urban hospitals report better quality score than those located in rural areas. Specifically, the first-difference regression results indicate that the quality of treatment for cardiovascular patients rises in the year following an increase in hospital profitability, financial leverage, and labor costs.ConclusionsThe results suggest that, when a hospital made more profit, had the capacity to finance investment using debt, paid higher wages presumably to attract more skilled nurses, its quality of care would generally improve. While the pursuit of profit induces hospitals to enhance both quantity and quality of services they offer, the lack of financial strength may result in a lower standard of health care services, implying the importance of monitoring the quality of care among those hospitals with poor financial health.

This paper finds noninterest income is positively correlated with the total systemic risk for U.S. banks. Decomposing total systemic risk into three components, we find that noninterest income is positively related to a bank’s tail risk, positively related to a bank’s interconnectedness risk, and an insignificantly related to a bank’s exposure to macroeconomic and finance factors. We also find that noninterest income is more volatile and negatively related to interest income. Finally, we find trading and other noninterest income to be positively correlated with systemic risk. Other noninterest income, compared with trading income, has a slightly larger economic impact. (JEL G01, G18, G20, G21, G32, G38) Received October 31, 2019; editorial decision February 3, 2020 by Editor Andrew Ellul.

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In this paper we present several new sequential Monte Carlo (SMC) algorithms for online estimation (filtering) of nonlinear dynamic systems. SMC has been shown to be a powerful tool for dealing with complex dynamic systems. It sequentially generates Monte Carlo samples from a proposal distribution, adjusted by a set of importance weight with respect to a target distribution, to facilitate statistical inferences on the characteristic (state) of the system. The key to a successful implementation of SMC in complex problems is the design of an efficient proposal distribution from which the Monte Carlo samples are generated. We propose several such proposal distributions that are efficient yet easy to generate samples from. They are efficient because they tend to utilize both the information in the state process and the observations. They are all Gaussian distributions hence are easy to sample from. The central ideas of the conventional nonlinear filters, such as extended Kalman filter, unscented Kalman filter and the Gaussian quadrature filter, are used to construct these proposal distributions. The effectiveness of the proposed algorithms are demonstrated through two applications-real time target tracking and the multiuser parameter tracking in CDMA communication systems.