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This work explores the relationship between corporate and economic growth within the United States since 1929. The corporate share of GDP climbed from 52.5 percent in 1929 to 59.7 percent in 2005. Depending upon the years included and the method of estimating respective growth rates, this increasing share of GDP accounts for up to 14 percent of real domestic corporate growth. However, the domestic corporate share of GDP can never exceed 100 percent. Subject to numerous assumptions, the models presented here estimate that this source of corporate growth could be exhausted as early as the year 2032. Given the lack of discussion of this issue in the relevant literature, it is unlikely that current stock valuations account for the eventual loss of this source of growth. The actual effect on stock prices of such a slowdown of domestic corporate growth will depend not only on how far into the future such an event occurs, but also on how successful these corporations are at finding new growth opportunities overseas. More research is needed to better model

During the 2005 North Atlantic hurricane season, an objective tropical cyclone vortex identification and tracking technique was applied to analyzed and forecast fields of three global operational numerical models- the National Centers for Environmental Prediction Global Forecast System (GFS), the Navy Operational Global Atmospheric Prediction System (NOGAPS), and the United Kingdom Meteorological Office model (UKMET). For the purpose of evaluating each model's performance with respect to forecasting tropical cyclone formation, 14 relevant parameters are cataloged for every tropical vortex. In this study, nine of the fourteen parameters are subjected to a linear discriminant analysis applied to all forecast vortices that exceed vorticity and warm core thresholds. The goal is to determine the combination of parameters for each model, at each 12-h forecast period to 120h, that best discriminates between a vortex that is correctly forecast to intensify into a tropical cyclone (developer) and a vortex that is forecast to intensify into a tropical cyclone, but does not (false alarm). The performance of the resulting discriminant functions are then assessed using the Heidke Skill Score and Receiver Operating Characteristic curves. Overall, the methodology applied to forecasts from the UKMET model shows the most skill with regard to identifying correct forecasts of tropical cyclone formation. / US Navy (USN) author.

Master of Agribusiness / Department of Agricultural Economics / Nathan P. Hendricks / Public parks can provide considerable value to the population that visit them, for the community around them and the local economy. A well designed public park can attract growth in tourism, stimulate a habitat for wildlife, contribute to personal health and wellness, improve the aesthetics of an area and stimulate economic growth. Managing and operating a public park entails many complex issues such as designing an attractive green space, implementing and maintaining the park, attracting and managing visitors and obtaining financial support. Public parks need to identify factors that influence park visitation in order to more effectively manage park visitorship.. This thesis examines park visitation analyzing data of park users of The High Line in New York City to develop a model to more accurately predict visitation. The thesis focuses on the critical social and climatic variables that attract visitors to spend time in the High Line park. Understanding these factors will allow park management the ability to create a strategic plan for managing a public space that best serves its visitors and the community. More specifically, a strategic plan helps to determine who the visitors are and what activities they enjoy in the park. In conceptualizing a solution, High Line can put into practice what its visitors want to see offered in the park and which of its programming needs improvement to attract more visitors. Meeting the needs of park visitors will create a better experience for the customers and a better management strategy for operations. A multivariate regression analysis was used to establish the relationship between High Line visitation and the climatic and social variables. The climatic variables of daily average temperature and precipitation. The social variables of day of the week and season of the year were added to the structural model as dummies. A time trend variable characterized as time in years was added to the model to show any yearly change in visitation to the park. This method has been widely applied to a number of studies testing the relationship of climatic and social variables to park visitation (Micah, Scotter and Fenech 2016). The results of this regression analysis show that the social variables of day of the week and season and the climatic variables of average temperature and precipitation had a significant affect on park visitation. The model developed can be used to forecast park visitation, quantifying the many variables that influence park visitation.

Studies of affective forecasting examine people’s ability to predict (forecast) their emotional (affective) responses to future events. Affective forecasts underlie nearly all decisions people make and the actions they take. However, people engage in systematic cognitive errors when making affective forecasts and most often overestimate the intensity and duration of their emotional responses. Understanding the mechanisms that lead to affective forecasting errors (e.g., immune neglect) and examining the utility of methods for improving affective forecasting errors (e.g., surrogation) can provide highly valuable information for clinicians as they assist clients in determining their goals both for therapy and for life. The first purpose of the current study was to determine if affective forecasting errors due to immune neglect lead to misjudgments about the relative emotional impact of minor versus moderate negative experiences (i.e., trauma severity). The second purpose was to examine the utility of surrogation for improving affective forecasts. Potential interaction effects between these two variables were also examined. The current study utilized a 2 (Trauma Severity: minor, moderate) X 3 (Prediction Information: surrogation information only, simulation information only, both types of information) experimental design. Undergraduates were recruited via the SONA system and randomly assigned to one of the six experimental conditions. A preliminary study was conducted to obtain surrogation information for use in the main study. All participants in the main study predicted how they would feel 10 minutes after receiving negative personality feedback, using a 10-point scale ranging from (1) very unhappy to (10) very happy. These predictions constitute their affective forecasts. All participants then actually received the negative personality feedback (ostensibly from another participant, a peer, in a nearby room) and reported their actual affective states ten minutes later, using the same scale. These ratings constitute their affective reports. Affective forecasting error was calculated as the difference between affective forecasts and affective reports. Results showed the affective forecasts of participants in the moderate trauma severity condition were significantly less accurate than those of participants in the minor trauma severity condition, providing evidence of immune neglect. Surrogation information significantly improved the accuracy of affective forecasts when participants were deprived of simulation information. Limitations of the current study and implications of the findings are discussed.

M.Com. (Economics) / Please refer to full text to view abstract

Summary in Chinese. / Thesis (M.B.A.)--Chinese University of Hong Kong. / Bibliography: leaves 65-67.

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