Influence measure for the L1 regression

Because outliers and leverage observations unduly affect the least squares regression, the identification of influential observations is considered an important and integrai part of the analysis. However, very few techniques have been developed for the residual analysis and diagnostics for the minimum sum of absolute errors, L₁ regression. Although the L₁ regression is more resistant to the outliers than the least squares regression, it appears that outliers (leverage) in the predictor variables may affect it. In this paper, our objective is to develop an influence measure for the L₁ regression based on the likelihood displacement function. We illustrate the proposed influence measure with examples.     

Inflated beta distributions

This paper considers the issue of modeling fractional data observed on [0,1), (0,1] or [0,1]. Mixed continuous-discrete distributions are proposed. The beta distribution is used to describe the continuous component of the model since its density can have quite different shapes depending on the values of the two parameters that index the distribution. Properties of the proposed distributions are examined. Also, estimation based on maximum likelihood and conditional moments is discussed. Finally, practical applications that employ real data are presented.     

Equivalence scales,the cost of children and household consumption patterns in Italy

This article deals with the estimation of parametric equivalence scales for Italian households with different demographic characteristics: composition, location and number of employed members in the household. Using a sample of 43,701 observations on monthly current expenditures from 1997 to 2004 we estimate a demand system for ten goods and we tackle the problem of corner solutions for some goods adopting the Two Step estimator proposed by Shonkweiler and Yen (Am J Agric Econ 81:972–982, 1999). The consumption behavior of households is also analyzed calculating compensated, uncompensated and expenditure elasticites for each commodity. By considering households that differ in composition (number of children), geographic location (four-different macro-areas of Italy), and number of employed adults, we allow for a range of useful comparisons.     

Incorporation,Transnationalism, and Gender: Immigrant Incorporation and Transnational Participation as Gendered Processes1

This article analyzes immigrant incorporation and transnational participation as gendered experiences. The results indicate that the incorporation of immigrants is a complex process affected negatively by class and racial exclusion and positively by their knowledge of U. S. society. The analysis also indicates that incorporation and transnational participation are concurrent and intertwined processes. Our results show that gender matters in the analysis of immigrant incorporation. The experiences of immigrant men and women share a lot in common as they confront similar challenges, but are also affected differently by the most relevant factors in the process of incorporation and transnational participation.     

Maximum likelihood estimation of a generalized star(p; 1p) model

Summary A STAR model is characterized by autoregressive terms lagged both in time and space. The model we call GSTAR presents also contemporaneous spatial correlation. Under the hypothesis of stationarity we derive conditional maximum likelihood estimators of the autoregressive parameters and a consistent estimator of their covariance matrix.     

The social consequences of transformation in Slovakia

This article provides basic information about the transformation process in Slovakia from 1989 to 1994. Presented are some of the major results of the SOCO survey and selected important social topics are analyzed. Sociological information about income, property, consumption, and expenditures of Slovak households is given, and the economic activities of Slovak households and peoples’ attitudes to the transformation process are discussed. The article also addresses who are the winners and losers of the transition in the context of rising poverty and changes in social policy.     

A proposal for categorizing the severity of non uniform differential item functioning: The polytomous case

Abstract

Differential item functioning (DIF) is present when something about the characteristics of a test taker interferes with the relationship between ability and item response. Non uniform DIF (NUDIF) exists when there is interaction between ability level and group membership. The aim of this study is to propose guidelines to evaluate the severity of NUDIF, taking into account the family of Rasch models. The severity of NUDIF is evaluated trying to quantify the overall biasing impact of the presence in the test of NUDIF items, on the estimated measure of the latent trait of interest.     

Modelling small and medium enterprise loan defaults as rare events: the generalized extreme value regression model

A pivotal characteristic of credit defaults that is ignored by most credit scoring models is the rarity of the event. The most widely used model to estimate the probability of default is the logistic regression model. Since the dependent variable represents a rare event, the logistic regression model shows relevant drawbacks, for example, underestimation of the default probability, which could be very risky for banks. In order to overcome these drawbacks, we propose the generalized extreme value regression model. In particular, in a generalized linear model (GLM) with the binary-dependent variable we suggest the quantile function of the GEV distribution as link function, so our attention is focused on the tail of the response curve for values close to one. The estimation procedure used is the maximum-likelihood method. This model accommodates skewness and it presents a generalisation of GLMs with complementary log–log link function. We analyse its performance by simulation studies. Finally, we apply the proposed model to empirical data on Italian small and medium enterprises.