Optimal cut-off for rare events and unbalanced misclassification costs

This paper develops a method for handling two-class classification problems with highly unbalanced class sizes and misclassification costs. When the class sizes are highly unbalanced and the minority class represents a rare event, conventional classification methods tend to strongly favour the majority class, resulting in very low detection of the minority class. A method is proposed to determine the optimal cut-off for asymmetric misclassification costs and for unbalanced class sizes. Monte Carlo simulations show that this proposal performs better than the method based on the notion of classification accuracy. Finally, the proposed method is applied to empirical data on Italian small and medium enterprises to classify them into default and non-default groups.     

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.     

Genetic algorithms for the analysis of Bayesian hierarchical partition models

Hierarchical partition models (see Malec and Sedransk, 1992, Consonni and Veronese, 1995) aim at finding an optimal grouping (partition) of a set of experiments regarding a target variable. In this class of models the partition is regarded as an unknown parameter, and one of the main goals is computing the posterior distribution over the class of the possible partitions. This problem has been addressed in Sampietro and Veronese (1998), where a Metropolis-Hastings algorithm is applied. In this paper the performance of an alternative procedure, based on the logic of genetic algorithms, is evaluated. The results of the two approaches are compared, even if a conjoint use of them is to be advised.     

Tests of Conditional Predictive Ability

We propose a framework for out‐of‐sample predictive ability testing and forecast selection designed for use in the realistic situation in which the forecasting model is possibly misspecified, due to unmodeled dynamics, unmodeled heterogeneity, incorrect functional form, or any combination of these. Relative to the existing literature (Diebold and Mariano (1995) and West (1996)), we introduce two main innovations: (i) We derive our tests in an environment where the finite sample properties of the estimators on which the forecasts may depend are preserved asymptotically. (ii) We accommodate conditional evaluation objectives (can we predict which forecast will be more accurate at a future date?), which nest unconditional objectives (which forecast was more accurate on average?), that have been the sole focus of previous literature. As a result of (i), our tests have several advantages: they capture the effect of estimation uncertainty on relative forecast performance, they can handle forecasts based on both nested and nonnested models, they allow the forecasts to be produced by general estimation methods, and they are easy to compute. Although both unconditional and conditional approaches are informative, conditioning can help fine‐tune the forecast selection to current economic conditions. To this end, we propose a two‐step decision rule that uses current information to select the best forecast for the future date of interest. We illustrate the usefulness of our approach by comparing forecasts from leading parameter‐reduction methods for macroeconomic forecasting using a large number of predictors.     

Is the Partner Who Decides to Divorce More Attractive? A Comparison Between Initiators and Noninitiators

Five hundred eighty divorced individuals from Germany, Italy, and Switzerland provided retrospective reports on the perceived similarities and differences between themselves and their former partner and on their own and their partner's attractiveness. Initiators and noninitiators were compared with regard to these different variables. Results demonstrated that most divorcees perceived themselves to be quite similar to their former partner. Initiating women were more likely than noninitiating women to perceive themselves as different from their former partner in personality, norms, and needs and to evaluate their former partner to be less attractive. Initiating men were more likely than noninitiating men to perceive differences with regard to the personality between themselves and their partners.     

JEEA‐FBBVA LECTURE 2013: THE NEW EMPIRICAL ECONOMICS OF MANAGEMENT

Over the last decade the World Management Survey (WMS) has collected firm‐level management practices data across multiple sectors and countries. We developed the survey to try to explain the large and persistent total factor productivity (TFP) differences across firms and countries. This review paper discusses what has been learned empirically and theoretically from the WMS and other recent work on management practices. Our preliminary results suggest that about a quarter of cross‐country and within‐country TFP gaps can be accounted for by management practices. Management seems to matter both qualitatively and quantitatively for performance at the level of the firm and the nation. Competition, governance, human capital, and informational frictions help account for the variation in management. We make some suggestions for both policy and future research.     

“Birds of a Feather” Fail Together: Exploring the Nature of Dependency in SME Defaults

This article studies the effects of incorporating the interdependence among London small business defaults into a risk analysis framework using the data just before the financial crisis. We propose an extension from standard scoring models to take into account the spatial dimensions and the demographic characteristics of small and medium‐sized enterprises (SMEs), such as legal form, industry sector, and number of employees. We estimate spatial probit models using different distance matrices based only on the spatial location or on an interaction between spatial locations and demographic characteristics. We find that the interdependence or contagion component defined on spatial and demographic characteristics is significant and that it improves the ability to predict defaults of non–start‐ups in London. Furthermore, including contagion effects among SMEs alters the parameter estimates of risk determinants. The approach can be extended to other risk analysis applications where spatial risk may incorporate correlation based on other aspects.     

Exploring sequences: a graphical tool based on multi-dimensional scaling

Summary.  Sequence analysis has become one of the most used and discussed tools to describe life course trajectories. We introduce a new tool for the graphical exploratory analysis of sequences. Our plots combine standard sequence plots with the results that are provided by multi-dimensional scaling. We apply our procedure to describe work and family careers of Israeli women by using data from the Israel Social Mobility Survey. We first focus on some preliminary choices relative to the definition of the sequences: the age span, the length of the sequences and the set of states registered in each time period. We then describe how our plots can be used to gain insights about the main features of sequences and about the relationships between sequences and external information.