Forecasting accuracy of behavioural models for participation in the arts

A first version of this paper was presented at the University of Catania, 2011 and at the fifth European Workshop on Applied Cultural Economics in Dublin, 2011.forecasting, count data, prediction intervals, Brier scores, bootstrapping, arts

Forecasting accuracy of behavioural models for participation in the arts

A first version of this paper was presented at the University of Catania, 2011 and at the fifth European Workshop on Applied Cultural Economics in Dublin, 2011.In this paper, we assess the forecasting performance of count data models applied to arts attendance. We estimate participation models for two artistic activities that differ in their degree of popularity -museum and jazz concerts- with data derived from the 2002 release of the Survey of Public Participation in the Arts for the United States. We estimate a finite mixture model – a zero-inflated negative binomial model - that allows us to distinguish “true” non-attendants and “goers” and their respective behaviour regarding participation in the arts. We evaluate the predictive (in-sample) and forecasting (out-of-sample) accuracy of the estimated models using bootstrapping techniques to compute the Brier score. Overall, the results indicate good properties of the model in terms of forecasting. Finally, we derive some policy implications from the forecasting capacity of the models, which allows for identification of target populations

Forecasting accuracy of behavioural models for participation in the arts

A first version of this paper was presented at the University of Catania, 2011 and at the fifth European Workshop on Applied Cultural Economics in Dublin, 2011.In this paper, we assess the forecasting performance of count data models applied to arts attendance. We estimate participation models for two artistic activities that differ in their degree of popularity -museum and jazz concerts- with data derived from the 2002 release of the Survey of Public Participation in the Arts for the United States. We estimate a finite mixture model – a zero-inflated negative binomial model - that allows us to distinguish “true” non-attendants and “goers” and their respective behaviour regarding participation in the arts. We evaluate the predictive (in-sample) and forecasting (out-of-sample) accuracy of the estimated models using bootstrapping techniques to compute the Brier score. Overall, the results indicate good properties of the model in terms of forecasting. Finally, we derive some policy implications from the forecasting capacity of the models, which allows for identification of target populations

Probabilistic Forecasting of Wind Power Ramp Events using Autoregressive Logit Models

A challenge for the efficient operation of power systems and wind farms is the occurrence of wind power ramps, which are sudden large changes in the power output from a wind farm. This paper considers the probabilistic forecasting of a ramp event, defined as exceedance beyond a specified threshold. We directly model the exceedance probability using autoregressive logit models fitted to the change in wind power. These models can be estimated by maximising a Bernoulli likelihood. We introduce a model that simultaneously estimates the ramp event probabilities for different thresholds using a multinomial logit structure and categorical distribution. To model jointly the probability of ramp events at more than one wind farm, we develop a multinomial logit formulation, with parameters estimated using a bivariate Bernoulli distribution. We use a similar approach in a model for jointly predicting one and two steps-ahead. We evaluate post-sample probability forecast accuracy using hourly wind power data from four wind farms

Prediction of inflows into Lake Kariba using a combination of physical and empirical models

Seasonal climate forecasts are operationally produced at various climate prediction centres around the world. However, these forecasts may not necessarily be objectively integrated into application models in order to help with decision-making processes. The use of hydro- meteorological models may be proven effective for reservoir operations since accurate and reliable prediction of reservoir inflows can provide balanced solution to the problems faced by dam or reservoir managers. This study investigates the use of a combination of physical and empirical models to predict seasonal inflows into Lake Kariba in southern Africa. Two predictions systems are considered. The first uses antecedent seasonal rainfall totals over the upper Zambezi catchment as predictor in a statistical model for estimating seasonal inflows into Lake Kariba. The second and more sophisticated method uses predicted low-level atmospheric circulation of a coupled ocean-atmosphere general circulation model (CGCM) downscaled to the inflows. Forecast verification results are presented for five run-on 3-month seasons; from September to June over an independent hindcast period of 14 years (1995/6 to 2008/9). Verification is conducted using the relative operating characteristic (ROC) and the reliability diagram. In addition to the presented verification statistics, the hindcasts are also evaluated in terms of their economic value as a usefulness indicator of forecast quality for bureaucrats and to the general public. The models in general perform best during the austral mid-summer season of DJF (seasonal onset of inflows) and the autumn season of MAM (main inflow season). Moreover, the prediction system that uses the output of the CGCM is superior to the simple statistical approach. An additional forecast of a recent flooding event (2010/11), which lies outside of the 14-year verification window, is presented to further demonstrate the forecast system’s operational capability during a season of high inflows that caused societal and infrastructure problems over the region.Applied Center for Climate and Earth Systems Science (ACCESS)http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1097-00882017-05-30hb201

A machine learning perspective on responsible gambling

Gamblers are frequently reminded to “gamble responsibly.” But these qualitative reminders come with no quantitative information for gamblers to judge relative product risk in skill-based gambling forms. By comparison, consumers purchasing alcohol are informed of product strength by alcohol by volume (ABV %) or similar labels. This paper uses mixed logistic regression machine learning to uncover the potential variation in soccer betting outcomes. This paper uses data from four bet types and eight seasons of English Premier League soccer, ending in 2018. Outcomes across each bet type were compared using three betting strategies: the most-skilled prediction, a random strategy, and the least-skilled prediction. There was a large spread in betting outcomes, with for example the per-bet average loss varying by a factor of 54 (from 1.1% to 58.9%). Gamblers’ losses were positively correlated with the observable betting odds across all bets, indicating that betting odds are one salient feature which could be used to inform gamblers about product risk. Such large differences in product risk are relevant to the promotion of responsible gambling

To what extent can new web-based technology improve forecasts? Assessing the economic value of information derived from Virtual Globes and its rate of diffusion in a financial market

As the rate of information availability increases, the ability to use web-based technology to improve forecasting becomes increasingly important. We examine Virtual Globe technology and show how the arrival of unprecedented types of web-based information enhances the ability to forecast and can lead to significant, measurable economic benefits. Specifically, we use market prices in a betting market over an eighteen-year period to examine how new elevation data from Virtual Globes (VG) enabled improved forecasting decisions and we explore how this information diffused through the betting market. The results demonstrate how short-lived, profitable opportunities arise from the arrival of novel information, and the speed at which markets adapt over time to account fully for new data

A new methodology for generating and combining statistical forecasting models to enhance competitive event prediction

Forecasting methods are routinely employed to predict the outcome of competitive events (CEs) and to shed light on the factors that influence participants’ winning prospects (e.g., in sports events, political elections). Combining statistical models’ forecasts, shown to be highly successful in other settings, has been neglected in CE prediction. Two particular difficulties arise when developing model-based composite forecasts of CE outcomes: the intensity of rivalry among contestants, and the strength/diversity trade-off among individual models. To overcome these challenges we propose a range of surrogate measures of event outcome to construct a heterogeneous set of base forecasts. To effectively extract the complementary information concealed within these predictions, we develop a novel pooling mechanism which accounts for competition among contestants: a stacking paradigm integrating conditional logit regression and log-likelihood-ratio-based forecast selection. Empirical results using data related to horseracing events demonstrate that: (i) base model strength and diversity are important when combining model-based predictions for CEs; (ii) average-based pooling, commonly employed elsewhere, may not be appropriate for CEs (because average-based pooling exclusively focuses on strength); and (iii) the proposed stacking ensemble provides statistically and economically accurate forecasts. These results have important implications for regulators of betting markets associated with CEs and in particular for the accurate assessment of market efficiency

Managerial Labor Mobility in the National Football League

Within the present dissertation, determinants of leader dismissals, promotions, and demotions are explored. A model of CEO dismissals is adapted to the context of the National Football League (NFL), whereby head coaches represent CEOs. Building upon empirical studies of the CEO dismissal model, a proxy is established which is representative of actual candidates to replace an executive rather than proxies based on industry and firm characteristics. Using the proxy for candidates provided statistically insignificant results that challenge the theoretical relationship between candidate availability and executive dismissals. Additionally, the present dissertation proposed and found empirical justification for incorporating an additional socio-political force within the empirically tested CEO dismissal model. Interestingly, within the deviant culture of the NFL, deviant behavior may increase or decrease executive dismissal likelihood depending on the type of deviant behavior, punishments received for deviant behavior, and implementation of institutional policies regarding deviant behavior. Finally, the present dissertation emphasized the relationship between executive dismissals and candidates available to succeed executives, and therefore, examined determinants of managerial promotions within the NFL. Specifically focusing on race and centrality as promotion and demotion determinants, the present dissertation found race and centrality to be statistically significant factors in promotions and demotions, though the influence of these variables depends on whether the manager is being considered for promotion or demotion as well as their current rank within the organization (i.e., upper- or lower-level management). Implications for practitioners and scholars as well as future research opportunities are also discussed