Erroneous perceptions and arousal among regular and occasional video poker players

This study investigated the relationship between the level of arousal (Heart Rate) and the number of erroneous perceptions among video poker players. Twelve regular and twelve occasional gamblers participated in a gambling session conducted in a natural environment. It was hypothesised that 1) a significant positive correlation would be observed between arousal and the number of erroneous verbalizations, 2) regular players would show a higher level of arousal than occasional players, and 3) they would emit more erroneous verbalizations. Results showed that hypotheses one and three were confirmed. Theoretical and practical implications of these results for the psychology of gambling are discussed.This study was supported by a grant from Loto-Québec.     

ANOTHER LOOK AT THE STRATIFICATION OF EDUCATIONAL TRANSITIONS: THE LOGISTIC RESPONSE MODEL WITH PARTIAL PROPORTIONALITY CONSTRAINTS

In this paper we reanalyze Robert D. Mare's highly influential work on educational transitions among American men born in the first half of the 20th century. Contrary to previous belief, Mare found that the effects of socioeconomic background variables decline regularly across educational transitions in conditional logistic regression analyses. We have reconfirmed Mare's findings and tested them by introducing a modified logistic response model that constrains selected social background effects to vary proportionally across educational transitions. We refer to our preferred model as the logistic response model with partial proportionality constraints (LRPPC). The model can easily be estimated in Stata or using other standard statistical software. Partial proportionality constraints may also prove useful in interpopulation comparisons based on other linear models.     

BAYESIAN SUBSET SELECTION AND MODEL AVERAGING USING A CENTRED AND DISPERSED PRIOR FOR THE ERROR VARIANCE

This article proposes a new data‐based prior distribution for the error variance in a Gaussian linear regression model, when the model is used for Bayesian variable selection and model averaging. For a given subset of variables in the model, this prior has a mode that is an unbiased estimator of the error variance but is suitably dispersed to make it uninformative relative to the marginal likelihood. The advantage of this empirical Bayes prior for the error variance is that it is centred and dispersed sensibly and avoids the arbitrary specification of hyperparameters. The performance of the new prior is compared to that of a prior proposed previously in the literature using several simulated examples and two loss functions. For each example our paper also reports results for the model that orthogonalizes the predictor variables before performing subset selection. A real example is also investigated. The empirical results suggest that for both the simulated and real data, the performance of the estimators based on the prior proposed in our article compares favourably with that of a prior used previously in the literature.     

The current state of the labor queue: National and regional Evidence

We employ vector autoregressive techniques to determine the current state of the labor queue. Unemployment rate differentials have narrowed in recent years, potentially due to a change in the queue ordering, though a tight labor market and a stable queue would yield similar results. We find no evidence that the queue ordering has changed, which brings into question the resiliency of gains made by minority groups. We employ the same techniques to reveal the state of the queue across geographic regions and find that substantial differences exist across regions, implying variation in the relative labor force status of demographic groups.     

Proportional reversed hazard rate model and its applications

The purpose of this paper is to study the structure and properties of the proportional reversed hazard rate model (PRHRM) in contrast to the celebrated proportional hazard model (PHM). The monotonicity of the hazard rate and the reversed hazard rate of the model is investigated. Some criteria of aging are presented and the inheritance of the aging notions (of the base distribution) by the PRHRM is studied. Characterizations of the model involving Fisher information are presented and the statistical inference of the parameters is discussed. Finally, it is shown that several members of the proportional reversed hazard rate class have been found to be useful and flexible in real data analysis.     

Bayesian analysis of a marked point process: Application in seismic hazard assessment

Point processes are the stochastic models most suitable for describing physical phenomena that appear at irregularly spaced times, such as the earthquakes. These processes are uniquely characterized by their conditional intensity, that is, by the probability that an event will occur in the infinitesimal interval (t, t+Δt), given the history of the process up tot. The seismic phenomenon displays different behaviours on different time and size scales; in particular, the occurrence of destructive shocks over some centuries in a seismogenic region may be explained by the elastic rebound theory. This theory has inspired the so-called stress release models: their conditional intensity translates the idea that an earthquake produces a sudden decrease in the amount of strain accumulated gradually over time along a fault, and the subsequent event occurs when the stress exceeds the strength of the medium. This study has a double objective: the formulation of these models in the Bayesian framework, and the assignment to each event of a mark, that is its magnitude, modelled through a distribution that depends at timet on the stress level accumulated up to that instant. The resulting parameter space is constrained and dependent on the data, complicating Bayesian computation and analysis. We have resorted to Monte Carlo methods to solve these problems.     

Social identities and political cleavages: the role of political context

Summary. Using a novel method, the paper investigates the influence of social group identities on attitudes and on voting in a variety of political contexts. Examining the major regions of Britain, Canada and the USA, we find considerable national and regional diversity in the nature of social cleavages. For example, social class and race had widely different effects across societies, but within societies their effects on attitudes and on voting were very similar. However, despite that, age and religion had a similar effect on attitudes across societies; the effects on voting varied considerably. The significant within-country differences underline the importance of using region, rather than country, as the unit of analysis. More importantly, these results highlight the role of political context, especially competing cleavages and the structure of party competition, in the establishment of politically relevant social cleavages.     

HEAVY-TAILED-DISTRIBUTED THRESHOLD STOCHASTIC VOLATILITY MODELS IN FINANCIAL TIME SERIES

To capture mean and variance asymmetries and time‐varying volatility in financial time series, we generalize the threshold stochastic volatility (THSV) model and incorporate a heavy‐tailed error distribution. Unlike existing stochastic volatility models, this model simultaneously accounts for uncertainty in the unobserved threshold value and in the time‐delay parameter. Self‐exciting and exogenous threshold variables are considered to investigate the impact of a number of market news variables on volatility changes. Adopting a Bayesian approach, we use Markov chain Monte Carlo methods to estimate all unknown parameters and latent variables. A simulation experiment demonstrates good estimation performance for reasonable sample sizes. In a study of two international financial market indices, we consider two variants of the generalized THSV model, with US market news as the threshold variable. Finally, we compare models using Bayesian forecasting in a value‐at‐risk (VaR) study. The results show that our proposed model can generate more accurate VaR forecasts than can standard models.