Swinger economics

Swinging is a sexual behaviour of increasing relevance but substantially ignored in theoretical economic investigation. This paper has two major goals. The first is to describe what swinging is, discuss its economic relevance and single out the main characteristics of swinger behaviour. To this end, the Italian situation has been considered as a type of case study. The second goal is to use standard and less-standard tools from economic theory to propose some preliminary assessments of the causes and consequences of swinger couples’ behaviour. In this respect, some contributions on two-sided markets, hedonic adaptation approaches and equilibrium matching models have proved particularly useful.     

Contextual Variability in Young Children's Gender Ingroup Stereotype

Self-categorization theorists (Oakes, Haslam & Turner, 1994) have shown that stereotypes are not rigid and fixed, and that they vary to reflect variations in the comparative context within which they are formed. In this paper we investigate stereotype variability in a sample of 6/7-year-old children. Participants describe a specific outgroup, and then describe the ingroup (that is 'boys' or 'girls', depending on the sex of the participant). There are two conditions: in condition one the outgroup is represented by adult 'men', if participants are boys, or by adult 'women', if participants are girls. In condition two the outgroup is represented by 'girls', if participants are boys, or by 'boys', if participants are girls. Results show that stereotypical traits attributed to the ingroup change significantly with changes in the frame of reference.     

A recursive approach for estimating missing observations in an univariate time series

A linear recursive technique that does not use the Kalman filter approach is proposed to estimate missing observations in an univariate time series. It is assumed that the series follows an invertible ARIMA model. The procedure is based on the restricted forecasting approach, and the recursive linear estimators are optimal in terms of minimum mean-square error.     

Minimum Distance Estimation of Search Costs Using Price Distribution

ABSTRACT

It has been shown that equilibrium restrictions in a search model can be used to identify quantiles of the search cost distribution from observedprices alone. These quantiles can be difficult to estimate in practice. This article uses a minimum distance approach to estimate them that is easy to compute. A version of our estimator is a solution to a nonlinear least-square problem that can be straightforwardly programmed on softwares such as STATA. We show our estimator is consistent and has an asymptotic normal distribution. Its distribution can be consistently estimated by a bootstrap. Our estimator can be used to estimate the cost distribution nonparametrically on a larger support when prices from heterogenous markets are available. We propose a two-step sieve estimator for that case. The first step estimates quantiles from each market. They are used in the second step as generated variables to perform nonparametric sieve estimation. We derive the uniform rate of convergence of the sieve estimator that can be used to quantify the errors incurred from interpolating data across markets. To illustrate we use online bookmaking odds for English football leagues’ matches (as prices) and find evidence that suggests search costs for consumers have fallen following a change in the British law that allows gambling operators to advertise more widely. Supplementary materials for this article are available online.     

An automatic classification and robust segmentation procedure of spatial objects

This paper proposes a statistical procedure for the automatic volumetric primitives classification and segmentation of 3D objects surveyed with high density laser scanning range measurements. The procedure is carried out in three main phases: first, a Taylor’s expansion nonparametric model is applied to study the differential local properties of the surface so to classify and identify homogeneous point clusters. Classification is based on the study of the surface Gaussian and mean curvature, computed for each point from estimated differential parameters of the Taylor’s formula extended to second order terms. The geometrical primitives are classified into the following basic types: elliptic, hyperbolic, parabolic and planar. The last phase corresponds to a parametric regression applied to perform a robust segmentation of the various primitives. A Simultaneous AutoRegressive model is applied to define the trend surface for each geometric feature, and a Forward Search procedure puts in evidence outliers or clusters of non stationary data. An erratum to this article can be found at     

How to cut a pizza fairly: Fair division with decreasing marginal evaluations

Existential and constructive solutions to the classic problems of fair division are known for individuals with constant marginal evaluations. By considering nonatomic concave capacities instead of nonatomic probability measures, we extend some of these results to the case of individuals with decreasing marginal evaluations. Received: 12 June 2001/Accepted: 23 May 2002 An earlier version of this paper, Caltech Social Science Working Paper # 1126, was titled “How to cut a cake healthily”. We wish to thank Maristella Botticini, Erio Castagnoli, Marco Dall'Aglio, Paolo Ghirardato, Luigi Montrucchio, Marco Scarsini, Marciano Siniscalchi, as well as Maurice Salles (the Editor), an Associate Editor, and four anonymous Referees for helpful suggestions. The financial support of MIUR, CNR, and Università Bocconi is gratefully acknowledged. Part of this research was done while the first author was visiting Boston University and the California Institute of Technology, which he thanks for their hospitality.     

On implementation of the Gibbs sampler for estimating the accuracy of multiple diagnostic tests

Implementation of the Gibbs sampler for estimating the accuracy of multiple binary diagnostic tests in one population has been investigated. This method, proposed by Joseph, Gyorkos and Coupal, makes use of a Bayesian approach and is used in the absence of a gold standard to estimate the prevalence, the sensitivity and specificity of medical diagnostic tests. The expressions that allow this method to be implemented for an arbitrary number of tests are given. By using the convergence diagnostics procedure of Raftery and Lewis, the relation between the number of iterations of Gibbs sampling and the precision of the estimated quantiles of the posterior distributions is derived. An example concerning a data set of gastro-esophageal reflux disease patients collected to evaluate the accuracy of the water siphon test compared with 24 h pH-monitoring, endoscopy and histology tests is presented. The main message that emerges from our analysis is that implementation of the Gibbs sampler to estimate the parameters of multiple binary diagnostic tests can be critical and convergence diagnostic is advised for this method. The factors which affect the convergence of the chains to the posterior distributions and those that influence the precision of their quantiles are analyzed.