The Demand for Lotto: The Role of Conscious Selection

This article presents estimates of the elasticity of demand for lottery tickets using time series data in which there is variation in the expected value of a lottery ticket induced by rollovers. An important feature of our data is that there are far more rollovers than expected given the lottery design. We find strong evidence that individuals do not choose their lottery numbers uniformly from a uniform distribution—that is, conscious selection. We use our estimates to derive the inverse supply function for the industry, and this enables us to identify the demand elasticity. We find the price elasticity to be close to unity, which implies that the operator is revenue maximizing—which is the regulator's objective.     

Modelling the probability distribution of prize winnings in the UK National Lottery: consequences of conscious selection

Summary.  In the statistical and economics literature on lotteries, the problem of designing attractive games has been studied by using models in which sales are a function of the structure of prizes. Recently the prize structure has been proxied by using the moments of the prize distribution. Such modelling is a vital input into the process of designing appealing new lottery games that can generate large revenues for good causes. We show how conscious selection, the process by which lottery players choose numbers non-randomly, complicates the multivariate distribution of prize winners by introducing massive overdispersion of numbers of winners, and large correlations between the numbers of different types of prize winner. Although it is possible intuitively to reach a qualitative understanding of the data, an a priori model does not fit well. We therefore construct an empirical model of the joint distribution of prize winners and use it to calculate the moments of ticket value as a function of sales. The new model gives much higher estimates of ticket value moments, particularly skewness, than previously obtained. Our results will have consequences for policy decisions regarding game design. A spin-off result is that, on the basis of the results of model fitting, lottery players may increase the expected value of their ticket by strategically choosing numbers which are less popular with other lottery players.     

博彩的公平度

在现实生活中,我们经常会碰到博彩的场合,如购买彩票、有奖游戏、认购新股以及各种形式的赌博,等等。本文所论的博彩范围与通常意义上的有些不同。所谓博彩,是指这样一种投资活动：每次活动的结果可能赚也可能亏,预先可以知道或估计有哪些可能的结果,但出现哪一个结...     

利用概率模型估算广东篮球彩票的中奖概率

2006年足球单场竞猜异常火爆,而全国联网篮彩却一直惨淡经营,竞猜型广东篮球彩票也只试行四个月就夭折,主要问题是篮球玩法和奖金设置不够合理.如何挽救低迷中的篮彩.是一个值得研究的课题,为使广东篮彩成为未来篮彩市场的主流.本文建议提高广东篮彩一等奖的返奖率,并给予保本优惠,再增设二等奖,在此基础上利用美国全国篮球联赛2004-2005赛季的数据建立概率模型来估计中奖概率,为奖金合理设置提供有益的参考.     

非均衡共变与内控冲击：福彩市场周期波动互谱分析

近年来,中国福彩市场出现不稳定波动现象。因相关理论研究缺乏,故发行机构无法准确把握运行规律而出现决策失灵。运用经过约束条件检验的互谱密度函数频域分析法测度1987—2009年间的GDP、城镇居民可支配收入、农村居民人均纯收入、体育彩票发行额及CPI这5个经济变量波动对福利彩票发行额周期波动的影响,结果发现：短期内福利彩票发行额只与GDP存在较强周期共变关系,相干谱系数是0.71;5个经济变量的互谱增益贡献比都在1%以下;体育彩票发行额以增量方式发挥短期非均衡冲击作用,比福彩发行额滞后3个月。     

The need for a federal lotteries review board

Three areas in the running of a lottery may be distinguished: the design of the prize structure, the marketing of the product, and the selection of the winners. The rules and regulations in each of these areas governing the large-scale Canadian lotteries are not clear. Some examples show the need for an independent body which would clarify these regulations and provide control over the large lottery operations in Canada.     

The use of a reference variety for comparisons in incomplete series of crop variety trials

In a series of crop variety trials, ‘test varieties’ are compared with one another and with a ‘reference’ variety that is included in all trials. The series is typically analyzed with a linear mixed model and the method of generalized least squares. Usually, the estimates of the expected differences between the test varieties and the reference variety are presented. When the series is incomplete, i.e. when all test varieties were not included in all trials, the method of generalized least squares may give estimates of expected differences to the reference variety that do not appear to accord with observed differences. The present paper draws attention to this phenomenon and explores the recurrent idea of comparing test varieties indirectly through the use of the reference. A new ‘reference treatment method’ was specified and compared with the method of generalized least squares when applied to a five-year series of 85 spring wheat trials. The reference treatment method provided estimates of differences to the reference variety that agreed with observed differences, but was considerably less efficient than the method of generalized least squares.     

Direct and Indirect Effects Based on Difference-in-Differences With an Application to Political Preferences Following the Vietnam Draft Lottery

Abstract

We propose a difference-in-differences approach for disentangling a total treatment effect within specific subpopulations into a direct effect and an indirect effect operating through a binary mediating variable. Random treatment assignment along with specific common trend and effect homogeneity assumptions identify the direct effects on the always and never takers, whose mediator is not affected by the treatment, as well as the direct and indirect effects on the compliers, whose mediator reacts to the treatment. In our empirical application, we analyze the impact of the Vietnam draft lottery on political preferences. The results suggest that a high draft risk due to the draft lottery outcome leads to an increase in mild preferences for the Republican Party, but has no effect on strong preferences for either party or on specific political attitudes. The increase in Republican support is mostly driven by the direct effect not operating through the mediator that is military service.     

The 1970 US draft lottery revisited: a spatial analysis

Summary.  We revise the result of the 1970 selective service draft lottery in the USA following an open question that was suggested by Fienberg in a paper published in Science in 1971. The result of the drawings can be viewed as a particular spatial pattern which can be analysed by using general spatial tools adapted to our context. Approaches for assessing the complete spatial randomness for this spatial process on a finite support are proposed. More specifically, these approaches involve the number of events in a square window and a k ( r )-based function used to analyse stationary spatial point processes.     

Kolmogorov–Smirnov,Fluctuation, and Z g Tests for Convergence of Markov Chain Monte Carlo Draws

We examine the sizes and powers of three tests of convergence of Markov Chain Monte Carlo draws: the Kolmogorov–Smirnov test, fluctuation test, and Geweke's test. We show that the sizes and powers are sensitive to the existence of autocorrelation in the draws. We propose a filtered test that is corrected for autocorrelation. We present a numerical illustration using the Federal funds rate.     

Marginal Likelihood Estimation with the Cross-Entropy Method

We consider an adaptive importance sampling approach to estimating the marginal likelihood, a quantity that is fundamental in Bayesian model comparison and Bayesian model averaging. This approach is motivated by the difficulty of obtaining an accurate estimate through existing algorithms that use Markov chain Monte Carlo (MCMC) draws, where the draws are typically costly to obtain and highly correlated in high-dimensional settings. In contrast, we use the cross-entropy (CE) method, a versatile adaptive Monte Carlo algorithm originally developed for rare-event simulation. The main advantage of the importance sampling approach is that random samples can be obtained from some convenient density with little additional costs. As we are generating independent draws instead of correlated MCMC draws, the increase in simulation effort is much smaller should one wish to reduce the numerical standard error of the estimator. Moreover, the importance density derived via the CE method is grounded in information theory, and therefore, is in a well-defined sense optimal. We demonstrate the utility of the proposed approach by two empirical applications involving women's labor market participation and U.S. macroeconomic time series. In both applications, the proposed CE method compares favorably to existing estimators.     

On inverse sampling without replacement

The inverse sampling (due to Haldane, 1945) was proposed for random sampling with replacement draws when “the numberr of successes in the sample until the process of drawing is stopped” is prefixed. In this paper an extension of that result is proposed when the draws are obtained via random sampling without replacement.     

Inference for a Class of Stochastic Volatility Models Using Option and Spot Prices: Application of a Bivariate Kalman Filter

In this paper Bayesian methods are applied to a stochastic volatility model using both the prices of the asset and the prices of options written on the asset. Posterior densities for all model parameters, latent volatilities and the market price of volatility risk are produced via a Markov Chain Monte Carlo (MCMC) sampling algorithm. Candidate draws for the unobserved volatilities are obtained in blocks by applying the Kalman filter and simulation smoother to a linearization of a nonlinear state space representation of the model. Crucially, information from both the spot and option prices affects the draws via the specification of a bivariate measurement equation, with implied Black–Scholes volatilities used to proxy observed option prices in the candidate model. Alternative models nested within the Heston (1993) framework are ranked via posterior odds ratios, as well as via fit, predictive and hedging performance. The method is illustrated using Australian News Corporation spot and option price data.     

Inference for a Class of Stochastic Volatility Models Using Option and Spot Prices: Application of a Bivariate Kalman Filter

In this paper Bayesian methods are applied to a stochastic volatility model using both the prices of the asset and the prices of options written on the asset. Posterior densities for all model parameters, latent volatilities and the market price of volatility risk are produced via a Markov Chain Monte Carlo (MCMC) sampling algorithm. Candidate draws for the unobserved volatilities are obtained in blocks by applying the Kalman filter and simulation smoother to a linearization of a nonlinear state space representation of the model. Crucially, information from both the spot and option prices affects the draws via the specification of a bivariate measurement equation, with implied Black-Scholes volatilities used to proxy observed option prices in the candidate model. Alternative models nested within the Heston (1993) framework are ranked via posterior odds ratios, as well as via fit, predictive and hedging performance. The method is illustrated using Australian News Corporation spot and option price data.     

Nonparametric density estimation for multivariate bounded data

We propose a new nonparametric estimator for the density function of multivariate bounded data. As frequently observed in practice, the variables may be partially bounded (e.g. nonnegative) or completely bounded (e.g. in the unit interval). In addition, the variables may have a point mass. We reduce the conditions on the underlying density to a minimum by proposing a nonparametric approach. By using a gamma, a beta, or a local linear kernel (also called boundary kernels), in a product kernel, the suggested estimator becomes simple in implementation and robust to the well known boundary bias problem. We investigate the mean integrated squared error properties, including the rate of convergence, uniform strong consistency and asymptotic normality. We establish consistency of the least squares cross-validation method to select optimal bandwidth parameters. A detailed simulation study investigates the performance of the estimators. Applications using lottery and corporate finance data are provided.     

A cluster‐sample approach for Monte Carlo integration using multiple samplers

A computational problem in many fields is to estimate simultaneously multiple integrals and expectations, assuming that the data are generated by some Monte Carlo algorithm. Consider two scenarios in which draws are simulated from multiple distributions but the normalizing constants of those distributions may be known or unknown. For each scenario, existing estimators can be classified as using individual samples separately or using all the samples jointly. The latter pooled‐sample estimators are statistically more efficient but computationally more costly to evaluate than the separate‐sample estimators. We develop a cluster‐sample approach to obtain computationally effective estimators, after draws are generated for each scenario. We divide all the samples into mutually exclusive clusters and combine samples from each cluster separately. Furthermore, we exploit a relationship between estimators based on samples from different clusters to achieve variance reduction. The resulting estimators, compared with the pooled‐sample estimators, typically yield similar statistical efficiency but have reduced computational cost. We illustrate the value of the new approach by two examples for an Ising model and a censored Gaussian random field. The Canadian Journal of Statistics 41: 151–173; 2013 © 2012 Statistical Society of Canada     

What Librarians Can Learn from Cable Television: Thoughts on Transformative Business Models and Their Impact on the Business of Libraries

This column explores three issues currently dominating the discourse in the electronic resources field—the big deal, perpetual access, and open access—and draws parallels to events and trends from outside libraries that may provide some insight into where we are headed.     

A Proof of the Asymptotic Equivalence of Two-Tail Probability Approximations

A modified efficient jump algorithm is proposed for the Markov Chain Monte Carlo draws of the exponential power distribution. Bayesian inference based on the exponential power error term and that on the normal error term are compared. Unbiasedness of the LAD estimator is proven.     

收入分配差异状况的一种新测度方法

在衡量收入分配差异方面,基尼系数存在较大局限。设计新的测度收入分配差异状况的方法十分必要。本文提出的对称收入系数及其派生指标能很好地运用于研究收入分配的差异及其变化状况。本文还结合我国城镇居民的收入分配状况进行分析,得到了一些新的结论。     

Bayesian Analysis of the Censored Regression Model with an AEPD Error Term

We present the censored regression model with the error term following the asymmetric exponential power distribution. We propose three Markov chain Monte Carlo (MCMC) algorithms: the first one uses the probability integral transformation; the second one uses a combination of the probability integral transformation and random walk draws; while the third one uses random walk draws. Using simulated data we compare the performance of the three MCMC algorithms. Then we compare the posterior means, or Bayes estimates, with maximum likelihood estimates. We estimate the stock option portion of executive compensation as an example of the empirical application.