Fan Identification in Professional Sport

The purpose of this research was to find out the relationship between professional sport fans and either the team or player they most support. The goal of the research was to find out whether fans of professional sport go out to games or watch games at their house because of their support for their favorite organization or their idol player. The reason behind the research was because out of all the psychology and sociology studies surrounding professional sport, 95% of all those studies included an athlete, team, or coach. Only 5% of all those mentioned studies were involved with the fan side. It is important to understand the reasoning fans have for each of their favorite leagues and why they watch the sport they love. Sports give every fan the opportunity of escapism. Whether at the game or watching from home, each game represents a new start for each fan base to rally for a win. The method used for drawing conclusions was through an anonymous survey with participants from a small college. The sample was completed through participants from age 18-23 with a mix of 56% male and 44% female. Along with this, fandom level was also surveyed to see what kind of professional sport fans were participating. What was found only established the idea more that the NFL is by far the most popular sport league in North America, and a hypothesis before the study was ran guessing the NFL is so popular because fantasy football is so widespread and allows for so many people to watch the games. It was believed fantasy football participation led to NFL viewing. However, what was learned in the survey was that fantasy football did not play as big a role on NFL fans participating as much as anticipated prior to the beginning of the research. What is known throughout the study is that the NFL and NBA are by far the more popular leagues compared to the other professional leagues in North America. Part of that reason is due to the fact that both those leagues do an exceptional job of promoting their players through other advertisements, while other leagues like MLB have their best player not promoting themselves to their full ability

The precession of eccentric discs in close binaries

We consider the precession rates of eccentric discs in close binaries, and compare theoretical predictions with the results of numerical disc simulations and with observed superhump periods. A simple dynamical model for precession is found to be inadequate. For mass ratios less than approximately 1/4 a linear dynamical model does provide an upper limit for disc precession rates. Theory suggests that pressure forces have a significant retrograde impact upon the precession rate (Lubow 1992). We find that the disc precession rates for three systems with accurately known mass ratios are significantly slower than predicted by the dynamical theory, and we attribute the difference to pressure forces. By assuming that pressure forces of similar magnitude occur in all superhumping systems, we obtain an improved fit to superhump observations.Comment: 6 pages to appear in MNRAS (accepted

Initial Expectations in New Keynesian Models with Learning

This paper examines how the estimation results for a standard New Keynesian model with constant gain least squares learning is sensitive to the stance taken on agents beliefs at the beginning of the sample. The New Keynesian model is estimated under rational expectations and under learning with three different frameworks for how expectations are set at the beginning of the sample. The results show that initial beliefs can have an impact on the predictions of an estimated model; in fact previous literature has exposed this sensitivity to explain the changing volatilities of output and inflation in the post-war United States. The results indicate statistical evidence for adaptive learning, however the rational expectations framework performs at least as well as the learning frameworks, if not better, in in-sample and out-of-sample forecast error criteria. Moreover, learning is not found to better explain time varying macroeconomic volatility any better than rational expectations. Finally, impulse response functions from the estimated models show that the dynamics following a structural shock can depend crucially on how expectations are initialized and what information agents are assumed to have.Learning, expectations, New Keynesian model, maximum likelihood

Learning and judgment shocks in U.S. business cycles

This paper examines the role of judgment shocks in combination with other structural shocks in explaining post-war economic volatility within the context of a New Keynesian model. Agents form expectations using constant gain learning then augment these forecasts with judgment. These judgments may be interpreted as a reaction to current news stories or policy announcements that would influence people's expectations. I allow for the possibility that these judgments be informatively based on information about structural shocks, but judgment itself may also be subject to its own stochastic shocks. I estimate a standard New Keynesian model that includes these shocks using Bayesian simulation methods. To aid in identifying expectational shocks from other structural shocks I include data on professional forecasts along with data on output gap, inflation, and interest rates. I find judgment is largely not informed by macroeconomic fundamentals; most of the variability in judgment is explained by its own stochastic shocks. Impulse response functions from the estimated model illustrate how shocks to judgment destabilize the economy and explain business cycle fluctuations.Learning; judgment; add-factors; New Keynesian model; Metropolis-Hastings

Regime Switching, Learning, and the Great Moderation

This paper examines the "bad luck" explanation for changing volatility in U.S. inflation and output when agents do not have rational expectations, but instead form expectations through least squares learning with an endogenously changing learning gain. It has been suggested that this type of endogenously changing learning mechanism can create periods of excess volatility without the need for changes in the variance of the underlying shocks. Bad luck is modeled into a standard New Keynesian model by augmenting it with two states that evolve according to a Markov chain, where one state is characterized by large variances for structural shocks, and the other state has relatively smaller variances. To assess whether learning can explain the Great Moderation, the New Keynesian model with volatility regime switching and dynamic gain learning is estimated by maximum likelihood. The results show that learning does lead to lower variances for the shocks in the volatile regime, but changes in regime is still significant in differences in volatility from the 1970s and after the 1980s.Learning, regime switching, great moderation, New Keynesian model, maximum likelihood