Have Betting Exchanges Corrupted Horse Racing?

Betting exchanges allow punters to bet on a horse to lose a race. This, many argue, has opened up the sport to a new form of corruption, where races will be deliberately lost in order to profit from betting. We examine whether anecdotal evidence of the fixing of horses to lose—of which there are many examples—is indicative of wider corruption. Following a “forensic economics” approach, we build an asymmetric information model of exchange betting and take it to betting data on 9,560 races run in 2013/2014. We find no evidence of the widespread corruption of horse racing by the betting exchanges

Bayesian Analysis of Deterministic Time Trend and Changes in Persistence Using a Generalised Stochastic Unit Root Model

This paper makes use of the novel Generalized Stochastic Unit Root (GSTUR) model, Bayesian model estimation and model comparison techniques to investigate the presence of a deterministic time trend in economic series. The model is specified to allow for changes in persistence over time, such as shifts from stationarity I(0) to nonstationarity I(1) or vice versa. This uncertainty raises the crucial question about how sure one can be that an economic time series has a deterministic trend when there is a change in the underlying properties. Empirical analysis indicates that the GSTUR model could provide new insights on time series studies.Stochastic Unit Root; MCMC; Bayesian

Bayesian Inference in a Stochastic Volatility Nelson-Siegel Model

In this paper, we develop and apply Bayesian inference for an extended Nelson- Siegel (1987) term structure model capturing interest rate risk. The so-called Stochastic Volatility Nelson-Siegel (SVNS) model allows for stochastic volatility in the underlying yield factors. We propose a Markov chain Monte Carlo (MCMC) algorithm to efficiently estimate the SVNS model using simulation-based inference. Applying the SVNS model to monthly U.S. zero-coupon yields, we find significant evidence for time-varying volatility in the yield factors. This is mostly true for the level and slope volatility revealing also the highest persistence. It turns out that the inclusion of stochastic volatility improves the model's goodness-of-fit and clearly reduces the forecasting uncertainty particularly in low-volatility periods. The proposed approach is shown to work efficiently and is easily adapted to alternative specifications of dynamic factor models revealing (multivariate) stochastic volatility.term structure of interest rates, stochastic volatility, dynamic factor model, Markov chain Monte Carlo

Bayesian Estimation and Model Selection in the Generalised Stochastic Unit Root Model

We develop Bayesian techniques for estimation and model comparison in a novel Generalised Stochastic Unit Root (GSTUR) model. This allows us to investigate the presence of a deterministic time trend in economic series, while allowing the degree of persistence to change over time. In particular the model allows for shifts from stationarity I(0) to nonstationarity I(1) or vice versa. The empirical analysis demonstrates that the GSTUR model provides new insights on the properties of some macroeconomic time series such as stock market indices, in ation and ex- change rates.Stochastic Unit Root, MCMC, Bayesian

Bayesian inference and forecasting in the stationary bilinear model

A stationary bilinear (SB) model can be used to describe processes with a time-varying degree of persistence that depends on past shocks. This study develops methods for Bayesian inference, model comparison, and forecasting in the SB model. Using monthly U.K. inflation data, we find that the SB model outperforms the random walk, first order autoregressive AR(1), and autoregressive moving average ARMA(1,1) models in terms of root mean squared forecast errors. In addition, the SB model is superior to these three models in terms of predictive likelihood for the majority of forecast observations

Selection and incentives in contests: evidence from horse racing

The designer of internal labour market promotion contests must balance the need to select the best candidate with the need to provide incentives for all candidates. We use an extensive data set from horse racing – where there is abundant variation in contest design features – to analyse if there are particular features that help to achieve these two objectives. We find that contests with higher prize money and fewer participants are the most successful at achieving the dual remit of selection and incentives

Biogas Production Potential and Kinetics of Microwave and Conventional Thermal Pretreatment of Grass

Pretreatment methods play an important role in the improvement of biogas production from the anaerobic digestion of energy grass. In this study, conventional thermal and microwave methods were performed on raw material, namely, Pennisetum hybrid, to analyze the effect of pretreatment on anaerobic digestion by the calculation of performance parameters using Logistic function, modified Gompertz equation, and transference function. Results indicated that thermal pretreatment improved the biogas production of Pennisetum hybrid, whereas microwave method had an adverse effect on the performance. All the models fit the experimental data with R-2&gt;0.980, and the Reaction Curve presented the best agreement in the fitting process. Conventional thermal pretreatment showed an increasing effect on maximum production rate and total methane produced, with an improvement of around 7% and 8%, respectively. With regard to microwave pretreatment, maximum production rate and total methane produced decreased by 18% and 12%, respectively.</p