The Effect of Recessions on Gambling Expenditures

This article examines the influence of the business cycle on expenditures of three major types of legalized gambling activities: Casino gambling, lottery, and pari-mutuel wagering. Empirical results are obtained using monthly aggregated US per capita consumption time series for the period 1959.01–2010.08. Among the three gambling activities only lottery consumption appears to be recession-proof. This series is characterized by a vast and solid growth that exceeds the growth in income and the growth in other gambling sectors. Casino gambling expenditures show a positive growth during expansions and no growth during recessions. Hence, the loss in income during recessions affects casino gambling. However, income shocks which are not directly related to the business cycle do not influence casino gambling expenditures. Pari-mutuel wagering displays an overall negative trend and its average growth rate is smaller than the growth in income, especially during recessions. The findings of this article provide important implications for the gambling industry and for local governments.     

Confidence bands from censored samples

Several types of asymptotic confidence bands have been proposed in the literature when the data are randomly censored on the right. Introducing new classes of bands, we place the old bands and their relationship to one another within a comprehensive theory of bands. A thorough analysis yields narrower bands and two kinds of modifications which are asymptotically distribution- and censor-free. One of these is useful when the interval on which the bands are constructed is predetermined and the width of the bands is random; the other, when there is a predetermined bound on the width and the interval is random. We illustrate our bands on the Szeged pacemaker data. These methods also provide a general modification of the Kolmogorov band in the uncensored case.     

Darling–Erdős limit results for change-point detection in panel data

We wish to test the null hypothesis if the means of N panels remain the same during the observation period of length T. A quasi-likelihood argument leads to self-normalized statistics whose limit distribution under the null hypothesis is double exponential. The main results are derived assuming that the each panel is based on independent observations and then extended to linear processes. The proofs are based on an approximation of the sum of squared CUSUM processes using the Skorokhod embedding scheme. A simulation study illustrates that our results can be used in case of small and moderate N and T. We apply our results to detect change in the “corruption index”.     

Diversity and assemblage filtering in ground-dwelling spiders (Araneae) along an urbanisation gradient in Denmark

Urban Ecosystems - We studied the effects on spiders of a three-step rural-urban urbanisation gradient near a Danish town embedded in a historically forested landscape. Using pitfall traps set in...     

Seasonal patterns in slot-machine gambling in Germany

Although several aspects of gambling have been thoroughly investigated, little is known about the effect of seasonality on gambling. This study investigated the seasonal patterns in slot-machine usage, based on a unique data set of slot-machine usage from a German gambling centre using time series analysis. Knowledge of seasonal slot-machine usage patterns provides useful insights for researchers, gambling centre managers and legal authorities. Slot-machine gambling activity appears to be highest in November, when poor weather is compounded with lack of entertainment activities and lowest in December, when ample entertainment possibilities may distract people from gambling. The estimated daily and weekly seasonal patterns support the self-control literature, which suggests that self-regulatory failures are more likely when people are more tired; after work, or late in the evening. The high variation in gambling during winter implies that the availability of alternative entertainment activities may have an important influence on slot-machine usage.     

On the reaction time of moving sum detectors

In this paper, we quantify the reaction time of on-line monitoring schemes for changes in the mean based on moving sums. The corresponding sequential test procedure requires a historical sample of size m   as a baseline, while decisions are made based on a window of size h=h(m)$h=h(m)$ containing the h most recent observations. Perhaps surprisingly, the limit distribution (obtained as m tends to infinity) of the associated stopping time crucially depends on the asymptotic relation of these two quantities, posing potential problems in applications. In the empirical part of the paper, we study therefore the finite sample behavior of the monitoring schemes. We provide tables of critical values for the various limit distributions and guidelines for practitioners via a simulation study.