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We study an alternative approach to determine the final league table in football competitions with a premature ending. For several countries, a premature ending of the 2019/2020 football season has occurred due to the COVID-19 pandemic. We propose a model-based method as a possible alternative to the use of the incomplete standings to determine the final table. This method measures the performance of the teams in the matches of the season that have been played and predicts the remaining non-played matches through a paired-comparison model. The main advantage of the method compared to the incomplete standings is that it takes account of the bias in the performance measure due to the schedule of the matches in a season. Therefore, the resulting ranking of the teams based on our proposed method can be regarded as more fair in this respect. A forecasting study based on historical data of seven of the main European competitions is used to validate the method. The empirical results suggest that the model-based approach produces more accurate predictions of the true final standings than those based on the incomplete standings.

We propose a basic high-dimensional dynamic model for tennis match results with time varying player-specific abilities for different court surface types. Our statistical model can be treated in a likelihood-based analysis and is capable of handling high-dimensional datasets while the number of parameters remains small. In particular, we analyze 17 years of tennis matches for a panel of over 500 players, which leads to more than 2000 dynamic strength levels. We find that time varying player-specific abilities for different court surfaces are of key importance for analyzing tennis matches. We further consider several other extensions including player-specific explanatory variables and the accountance of specific configurations for Grand Slam tournaments. The estimation results can be used to construct rankings of players for different court surface types. We finally show that our proposed model can also be effective in forecasting. We provide evidence that our model significantly outperforms existing models in the forecasting of tennis match results.

This article deals with the results of a study that focussed on the political capital created by abgeordnetenwatch.de (AW). AW is a web-platform that aims to promote a dialogue between citizens and politicians, increase political transparency, and limit the influence of lobbyists on politics. It does so by a spectrum of activities ranging from offering a public forum for politi-cal exchange, to documenting politician’s voting behaviour, to initiating legal action against misconduct. 745 citizens and 255 politicians participated in an online survey to express their views on the effects of AW. Some comparisons have been drawn to data from the European Social Survey (ESS), both as regards traits of AW users against the general population and in assessing AW’s effects. My research finds that AW users have higher levels of literacy and engagement than ESS respondents who are representative of the general population, and only part of the differences are due to different inclinations of the two groups. AW users are also politically more conscious (cultural capital) than the population. AW users express much more positive views than politicians about the platform’s effects in terms of information, awareness raising, exposure of politicians to the public and sensitisation for lobbyism. Given the positive assessment of impacts by citizens, it is striking that politicians are rather indifferent towards the platform’s effects. This applies even more so as activity on AW seems a replacement for rather than a way into political engagement within formal organisations.

This paper proposes a novel approach to introduce time-variation in structural parameters of DSGE models. Structural parameters are allowed to evolve over time via an observation-driven updating equation. The estimation of the resulting DSGE model can be easily performed by maximum likelihood without the need of time-consuming simulation-based methods. An application to a DSGE model with time varying volatility for structural shocks is presented. The results indicate a significant improvement in forecasting performance.

We first consider an extension of the generalized autoregressive conditional heteroskedasticity (GARCH) model that allows for a more flexible weighting of financial squared-returns for the filtering of volatility. The parameter for the squared-return in the GARCH model is time-varying with an updating function similar to GARCH but with the squared-return replaced by the product of the volatility innovation and its lagged value. This local estimate of the first order autocorrelation of volatility innovations acts as an indicator of the importance of the squared-return for volatility updating. When recent volatility innovations have the same sign (positive autocorrelation), the current volatility estimate needs to adjust more quickly than in a period where recent volatility innovations have mixed signs (negative autocorrelation). The empirical relevance of the accelerated GARCH updating is illustrated by forecasting daily volatility in return series of all individual stocks present in the Standard & Poor’s 500 index. Major improvements are reported for those stock return series that exhibit high kurtosis. The local adjustment in weighting new observational information is generalised to score-driven time-varying parameter models of which GARCH is a special case. It is within this general framework that we provide the theoretical foundations of accelerated updating. We show that acceleration in updating is more optimal in terms of reducing Kullback-Leibler divergence and in comparison to fixed updating. The robustness of our proposed extension is highlighted in a simulation study within a misspecified modelling framework. The score-driven acceleration is also empirically illustrated with the forecasting of US inflation using a model with time-varying mean and variance; we report significant improvements in the forecasting accuracy at a yearly horizon.

This paper investigates how the perceived risk that the euro area will experience deflation has evolved over time, and what this risk implies for the credibility of the ECB. We use a novel data set on market participants’ perceptions of short- to long-term deflation risk implied by year-on-year options on forward inflation swaps. We investigate whether long-term inflation expectations have become de-anchored, by studying whether long-term deflation risk has been affected by changes in oil prices and by short-term deflation risk. Our analysis suggests that the anchoring properties of euro area inflation expectations have weakened, albeit in a still subtle way.

Invertibility conditions for observation-driven time series models often fail to be guaranteed in empirical applications. As a result, the asymptotic theory of maximum likelihood and quasi-maximum likelihood estimators may be compromised. We derive considerably weaker conditions that can be used in practice to ensure the consistency of the maximum likelihood estimator for a wide class of observation-driven time series models. Our consistency results hold for both correctly specified and misspecified models. The practical relevance of the theory is highlighted in a set of empirical examples. We further obtain an asymptotic test and confidence bounds for the unfeasible “true” invertibility region of the parameter space.

This paper investigates the impact of the “unconventional” monetary policy measures taken by the Eurosystem on both the unsecured and the secured money markets. Furthermore, we provide insight into the shifts between the unsecured and secured markets. We provide a euro area overview and a Core-versus-Periphery breakdown. Our results show that: 1) there is a clear segmentation between Core and Periphery; 2) the use of the unsecured money market has decreased substantially and is no longer representative as a reflection of the euro area as a whole; and 3) the use of the secured money markets has increased substantially in value terms since the start of the crisis. Both the secured and the unsecured money markets reacted strongly to the first 3-year long term refinancing operations and quantitative easing. It is not to be expected that turnover in the money markets will revert to pre-crisis levels, in part because new regulation, such as the Basel III requirements, dissuades banks from engaging in short-term lending. Therefore, monetary policy experts should also devote their attention to steering the rates in the secured money market.

Scholars currently discuss various concepts affecting the social sphere. One of the most prominent phenomena is the one of Social Entrepreneurship (SE). Due to the fact that definitions of the term get increasingly precise, there is less remaining confusion about the meaning of SE itself, however, many related discussions are taking place simultaneously pointing into a similar direction. Neighboring concepts covered by scholarly research include shifts in the ‘traditional’ nonprofit sector, the Social Business concept, ‘strategic Corporate Social Responsibility’ and ‘Base-of-the-Pyramid’ business. It becomes evident that we witness an increasing tendency towards hybridity that surpasses boundaries of sectors traditionally held distinct – a development regularly referred to as a ‘blurring of boundaries’ between the spheres of the state, the market and civil society. This paper aims at shedding some more light on the field of social impact activity and at putting existing concepts in relation to each other by conceptualizing a ‘Social Entrepreneurship Scheme’. The scheme shall also serve as a tool to enhance studies of ‘Social Entrepreneurial Organizations’ by offering a framework for integrating the examined entities into the larger context.