In Search of Executive Impairment in Pathological Gambling: A Neuropsychological Study on Non-treatment Seeking Gamblers

Pathological gambling is characterized by a persisting maladaptive and recurrent behavior with severe social and psychological consequences. There is evidence of strong comorbidity with psychiatric manifestations as well as cognitive mainly involving executive functions. This study aimed to investigate impairment in executive functions and working memory, and personality traits in a sample of Greek gamblers. Twenty-four men involved in various gambling activities were recruited from ecological settings as probable pathological gamblers. They were assessed with a comprehensive neuropsychological battery involving several executive tasks, the Zuckerman–Kuhlman Personality Questionnaire, the Hospital Anxiety Depression Scale, and the Difficulties in Emotion Regulation Scale. An age- and education-level matched group of 21 men without history of habitual gambling served as controls. As a group, gamblers displayed significantly lower scores on indices of inhibition, decision making and self-reported emotional awareness, and scored higher on impulsivity/sensation seeking personality traits. Notably, gamblers scored similarly or significantly higher on measures of verbal and visuospatial working memory, cognitive flexibility, processing speed, verbal fluency, and sustained attention. Overall, we argue that gamblers do present with specific cognitive deficits, but there is no evidence for a generalized executive impairment, and further stress the importance of investigating cognitive, personality, and psychiatric aspects of gambling on the basis of an ecologically valid sampling.     

Tests for Structural Changes in Time Series of Counts

We propose methods for detecting structural changes in time series with discrete‐valued observations. The detector statistics come in familiar L2‐type formulations incorporating the empirical probability generating function. Special emphasis is given to the popular models of integer autoregression and Poisson autoregression. For both models, we study mainly structural changes due to a change in distribution, but we also comment for the classical problem of parameter change. The asymptotic properties of the proposed test statistics are studied under the null hypothesis as well as under alternatives. A Monte Carlo power study on bootstrap versions of the new methods is also included along with a real data example.     

Fourier–type tests involving martingale difference processes

We develop testing procedures which detect if the observed time series is a martingale difference sequence. Furthermore, tests are developed that detect change–points in the conditional expectation of the series given its past. The test statistics are formulated following the approach of Fourier–type conditional expectations first proposed by Bierens (1982 Bierens, H. J. (1982). Consistent model speci?cation tests. J. Econometr. 20:105–134.[Crossref], [Web of Science ®] , [Google Scholar]) and have the advantage of computational simplicity. The limit behavior of the test statistics is investigated under the null hypothesis as well as under alternatives. Since the asymptotic null distribution contains unknown parameters, a bootstrap procedure is proposed in order to actually perform the test. The performance of the bootstrap version of the test is compared in finite samples with other methods for the same problem. A real–data application is also included.     

Estimating the parameters of Poisson-exponential models

This paper proposes two methods of estimation for the parameters in a Poisson-exponential model. The proposed methods combine the method of moments with a regression method based on the empirical moment generating function. One of the methods is an adaptation of the mixed-moments procedure of Koutrouvelis & Canavos (1999). The asymptotic distribution of the estimator obtained with this method is derived. Finite-sample comparisons are made with the maximum likelihood estimator and the method of moments. The paper concludes with an exploratory-type analysis of real data based on the empirical moment generating function.     

Diagnostic tests for the distribution of random effects in multivariate mixed effects models

Abstract

Fourier methods are proposed for testing the distribution of random effects in classical and robust multivariate mixed effects models. The test statistics involve estimation of the characteristic function of random effects. Theoretical and computational issues are addressed while Monte Carlo results show that the new procedures compare favorably with other methods.     

Sticky Price Models of the Business Cycle: Can the Contract Multiplier Solve the Persistence Problem?

We construct a quantitative equilibrium model with firms setting prices in a staggered fashion and use it to ask whether monetary shocks can generate business cycle fluctuations. These fluctuations include persistent movements in output along with the other defining features of business cycles, like volatile investment and smooth consumption. We assume that prices are exogenously sticky for a short time. Persistent output fluctuations require endogenous price stickiness in the sense that firms choose not to change prices much when they can do so. We find that for a wide range of parameter values, the amount of endogenous stickiness is small. Thus, we find that in a standard quantitative model, staggered price‐setting, alone, does not generate business cycle fluctuations.     

Adaptive estimation of the center of symmetry based on the empirical characteristic function

A procedure based on the empirical characteristic function is proposed for the estimation of the center of symmetric distributions. The method is an adaptive modification of the procedure proposed by Koutrouvelis (1985). The asymptotic behavior of the resulting estimator is investigated and finite sample comparisons are made with the previous nonadaptive estimator and an adaptive trimmed mean proposed by Hogg (1974).     

Validation tests for semi-parametric models

Tests are proposed for validation of the hypothesis that a partial linear regression model adequately describes the structure of a given data set. The test statistics are formulated following the approach of Fourier-type conditional expectations first suggested by Bierens [Consistent model specification tests. J Econometr. 1982;20:105–134]. The proposed procedures are computationally convenient, and under fairly mild conditions lead to consistent tests. Corresponding bootstrap versions are compared with alternative procedures for a wide selection of different estimators of the underlying partial linear model.     

A new goodness-of-fit test for certain bivariate distributions applicable to traffic accidents

T. Cacoullos and H. Papageorgiou [On some bivariate probability models applicable to traffic accidents and fatalities, Int. Stat. Rev. 48 (1980) 345–356] studied a special class of bivariate discrete distributions appropriate for modeling traffic accidents, and fatalities resulting therefrom. The corresponding random variable may be written as $Z={(N,Y)}^{\prime }$, with $Y={\sum }_{j=1}^N{X}_j$, where ${\left\{X_j\right\}}_{j=1}^N$, are independent copies of a (discrete) random variable $X$, and $N$ is independent of ${\left\{X_j\right\}}_{j=1}^N$, and follows a Poisson law. If $X$ follows a Poisson law (resp. Binomial law), the resulting distribution is termed Poisson–Poisson (resp. Poisson–Binomial). L2-type goodness-of-fit statistics are constructed for the ‘general distribution’ of this kind, where $X$ may be an arbitrary discrete nonnegative random variable. The test statistics utilize a simple characterization involving the corresponding probability generating function, and are shown to be consistent. The proposed procedures are shown to perform satisfactorily in simulated data, while their application to accident data leads to positive conclusions regarding the modeling ability of this class of bivariate distributions.