This June one of the biggest and most popular sports tournaments will take place in Austria and Switzerland, the European soccer championship 2008 (UEFA EURO 2008). Therefore millions of soccer fans in Europe and throughout the world are asking themselves: "Who is going to win the EURO 2008?" Many people, including sports experts and former players, give their guesses and expectations in the media, but there is also a group with financial incentives, like some economists who expect economical increases for the country of the winning team and bookmakers and their customers who directly make money with their beliefs. Some predictions are only guesses, but other predictions are based on quantitative methods, such as the studies of UBS Wealth Management Research Switzerland and the Raiffeisen Zentralbank. In this report we will introduce a new method for predicting the winner. Whereas other prediction methods are based on historical data, e.g., the Elo rating, or the FIFA/Coca Cola World rating, our method is based on current expectations, the bookmakers odds for winning the championship. In particular we use the odds for winning the championship for each of the 16 teams of 45 international bookmakers. By interpreting these odds as rating of the expected strength of the teams by the bookmakers, we derive a consensus rating by modelling the log-odds using a random-effects model with a team-specific random effect and a bookmaker-specific fixed effect. The consensus rating of a team can be used as an estimator for the unknown "true" strength of a team. Our method predicts team Germany with a probability of about 18.7% as the EURO 2008 winner. We predict also that the teams playing the final will be Germany and Spain with a probability of 13.9%, where Germany will win with a probability of 55%. In our study, Italy, the favorite according to the current FIFA/Coca Cola World ranking and Elo ranking, has a much lower probability than these teams to win the tournament: only 10.6%. The defending champion Greece has low chances to win the title again: about 3.4%. Furthermore, the expected performance of the host countries, Austria and Switzerland, is much better in the bookmakers consensus than in the retrospective Elo and FIFA/Coca Cola World ratings, i.e., indicating an (expected) home court advantage. Despite the associated increase in the winning probabilities, both teams have rather poor chances to win the tournament with probabilities of 1.3% and 4.0%, respectively. In a group effect study we investigate how much the classification into the four groups (A-D) affects the chance for a team to win the championship. / Series: Research Report Series / Department of Statistics and Mathematics

The purpose of the simulation was to assess the impact of weight pattern assignment when using the multiple membership random effects model (MMREM). In contrast with most previous methodological research using the MMREM, mobility was not randomly assigned; rather the likelihood of student mobility was generated as a function of the student predictor. Two true weights patterns were used to generate the data (random equal and random unequal). For each set of generated data, the true correct weights and two incorrect fixed weight patterns (fixed equal and fixed unequal) that are similar to those used in practice by applied researchers were used to estimate the model. Several design factors were manipulated including the percent mobility, the ICC, and the true generating values of the level one and level two mobility predictors. To assess parameter recovery, relative parameter bias was calculated for the fixed effects and random effects variance components. Standard error (SE) bias was also calculated for the standard errors estimated for each fixed effect. Substantial relative parameter bias differences between weight patterns used were observed for the level two school mobility predictor across conditions as well as the level two random effects variance component, in some conditions. Substantial SE bias differences between weight patterns used were also found for the school mobility predictor in some conditions. Substantial SE and parameter bias was found for some parameters for which it was not anticipated. The results, discussion, future directions for research, and implications for applied researchers are discussed.

This study was undertaken in response to concerns that the decreasing levels of funding for government tree planting cost share programs will result in significant reductions in non-industrial private tree planting efforts in the South. The purpose of this study is to quantify how the funding of various cost share programs, and market signals interact and affect the level of private tree planting. The results indicate that the ACP, CRP, and Soil Bank programs have been more influential than the FIP, FRM, FSP, SIP, and State run subsidy programs. Reductions in the CRP funding will result in less tree planting; while it is not clear that funding reductions in FIP, or other programs targeted toward reforestation after harvest, will have a negative impact on tree planting levels. / Master of Science

The purpose of this paper is to investigate how the changes in trade values are affected by the implementation of the euro currency. We study the EU members, including 11 EMU members and 3 non-EMU members (Sweden, Denmark and the United Kingdom). The empirical analysis is conducted by using a modified version of the standard gravity model. Our core findings can be summarized into two parts. First, the euro effect on trade which is estimated by the euro-dummy coefficient reflects an adverse influence by the euro creation on trade values for the first two years of the implementation on all our sample countries. It leads us to a conclusion that there is no significant improvement of trade in the year of implementation. These results do not change when a time trend variable is added to evaluate the robustness of the model. Our primary interpretation is that the euro creation does not have an immediate impact on trade but it is rather gradual as countries need time to adapt to a new currency. It is connected to our second finding that the negative influence of the euro implementation is not permanent but eventually initiates positive outcomes on trade values over time, thus concluding that the euro implementation has had gradual impact on both EMU and non-EMU members.

In this paper we carry out fully Bayesian analysis of the general heterogeneity model, which is a mixture of random effects model, and its special cases, the random coefficient model and the latent class model. Our application comes from Conjoint analysis and we are especially interested in what is gained by the general heterogeneity model in comparison to the other two when modeling consumers' heterogeneous preferences. (author's abstract) / Series: Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science"

This dissertation describes a minimum distance method for density estimation when the variable of interest is not directly observed. It is assumed that the underlying target density can be well approximated by a mixture of normals. The method compares a density estimate of observable data with a density of the observable data induced from assuming the target density can be written as a mixture of normals. The goal is to choose the parameters in the normal mixture that minimize the distance between the density estimate of the observable data and the induced density from the model. The method is applied to the deconvolution problem to estimate the density of $X_{i}$ when the variable $% Y_{i}=X_{i}+Z_{i}$, $i=1,\ldots ,n$, is observed, and the density of $Z_{i}$ is known. Additionally, it is applied to a location random effects model to estimate the density of $Z_{ij}$ when the observable quantities are $p$ data sets of size $n$ given by $X_{ij}=\alpha _{i}+\gamma Z_{ij},~i=1,\ldots ,p,~j=1,\ldots ,n$, where the densities of $\alpha_{i} $ and $Z_{ij}$ are both unknown. The performance of the minimum distance approach in the measurement error model is compared with the deconvoluting kernel density estimator of Stefanski and Carroll (1990). In the location random effects model, the minimum distance estimator is compared with the explicit characteristic function inversion method from Hall and Yao (2003). In both models, the methods are compared using simulated and real data sets. In the simulations, performance is evaluated using an integrated squared error criterion. Results indicate that the minimum distance methodology is comparable to the deconvoluting kernel density estimator and outperforms the explicit characteristic function inversion method.

Many studies suggest the ability of the NAIC Risk-Based Capital ratio (RBC ratio) to predict insurer insolvency. Based on the US life insurer (insurer) data for the period of 2005 to 2008, this study finds explanatory variables that have a statistically significant relationship with the RBC ratio. Advantages of panel data over cross-sectional and time series data analysis are exploited to make valid inference on coefficients of the explanatory variables. Testing for unobserved insurer and time effects and for dependence between these effects and the explanatory variables indicates the appropriateness of the fixed insurer and time effects model. Based on the ordinary least squares estimates, it is found that insurers' size, capital-to-asset ratio, and return on capital have a statistically significant relationship with the RBC ratio. Additionally, health product, annuity product, opportunity, and regulatory risks of insurers are related to the RBC ratio. Accounting for heteroscedasticity and autocorrelation for a given insurer yields the same coefficient estimates, but increased standard errors. / text

This thesis investigates why and how macroeconomic volatility in source countries interacts with their FDI outflows. The study focuses on FDI flowing out from OECD countries to less developed countries in the ASEAN region. Using a panel data encompassing 52 country-pairs over the period 1996-2011, I find a negative correlation between FDI outflows and macroeconomic volatility in source countries. More specifically the empirical results suggest an adverse relationship between inflation and output volatility (business cycles fluctuations) and FDI flows – the more macroeconomic volatility in developed economies the lesser FDI flows to less developed economies, which is explained by Keynesian theories. These findings derive from a gravity model approach, which enabled me to control for host country determinants. In order to estimate these relationships I adopted a random effects model and a tobit model. The reason behind the use of these two models derives from the different views within this branch of research because of censored FDI statistics. The thesis is inspired by Éric Rougier’s et al. work on how macroeconomic volatility in European countries interacts with FDI flows to the MENA region (2012).

Reliability measures have been well studied over many years, beginning with an entire chapter devoted to intraclass correlation in the first edition of Fisher (1925). Such measures have been thoroughly studied for two factor models. This dissertation, motivated by a medical research problem, extends point and confidence interval estimation of both intraclass correlation coefficient and interater reliability coefficient to models containing three crossed random factors -- subjects, raters and occasions. The intraclass correlation coefficient is used when decision is made on an absolute basis with rater's scores, while the interater reliability coefficient is defined for decisions made on a relative basis. The estimation is conducted using both ANOVA and MCMC methods. The results from the two methods are compared. The MCMC method is preferred for analyses of small data sets when ICC values are high. Besides, the bias of estimator of intraclass correlation coefficient in one-way random effects model is evaluated. / Statistics

African countries have over the last few decades, experienced a thorny path towards sustained economic growth. Quite a number of researchers have opined that a major factor responsible for their stunted growth path is the prevalence of corruption in the governments of many African countries. However, a group of scholars, called revisionists, have suggested that corruption actually acts as grease in the wheel that ensures the smooth running of an economy, by providing a mechanism to evade inefficient bureaucratic procedures and allow more equitable representation of minority members of the society. With the increasing exposure of African economies to the international community, there is a need to examine the obtainable evidence in relation to corruption and economic growth in African countries. This thesis, therefore, aims to establish the nature of the relationship between corruption and economic growth in the selected African countries. The growth rate of gross domestic product per capita is used to represent the variable, economic growth. The study employs the use of panel data fixed effects and random effect estimation techniques, across 18 countries, over the period of 1997 – 2016. The results show that corruption has a positive relationship with economic growth in the selected African countries. This is in line with the grease in the wheel argument for corruption proposed by revisionists. The results also indicate that corruption has a moderately significant impact on economic growth at 10% level of significance. The literature review suggests that corruption affects economic growth directly and indirectly through mechanisms such as investment (private and public), human capital, openness, and institutional mechanisms, among others.