CRIME AND POVERTY: A SEARCH‐THEORETIC APPROACH*

Numerous studies document that criminal activity is positively related to unemployment and negatively related to educational attainment levels within given communities. We study this phenomenon in the context of a search‐equilibrium model, in which agents choose between formal employment and pursuing crime‐related activities (theft). Prior to their “occupational choices,” agents undertake costly schooling, raising their productivity. Crime acts, in essence, as a tax on human capital by affecting the probability that a worker's earnings (possessions) are subsequently appropriated. There are multiple equilibria. High crime, low levels of educational attainment, long spells of unemployment, and poverty are correlated across them.     

The Contingent Value of Responsive and Proactive Market Orientations for New Product Program Performance*

While the benefits of being market oriented are largely accepted, a group of scholars and managers remain skeptical. Marketing scholars have sought to counter the criticisms leveled against market orientation (MO) by arguing that it has both responsive and proactive dimensions. However, few studies have empirically examined the complexity of the effects of these dimensions on firm performance. Drawing on theories of resource‐based advantage and organizational search behavior, this article advances understanding by arguing that responsive and proactive market orientations have curvilinear effects on product development performance, that their interaction may be positively related to product development performance, and that their effects on new product program performance are moderated differentially by the organizational implementation conditions and marketing function power. Survey results of 175 U.S. firms indicate support for most of the hypotheses. Specifically, whereas responsive MO has a U‐shaped relationship with new product program performance, proactive MO has an inverted U‐shaped relationship with new product program performance. Contrary to the arguments presented here, the interaction of both orientations is negatively related to new product program performance. This study finds that both orientations are needed; however, new product program performance is enhanced when one is at higher level and the other is at lower level. Finally, responsive MO is only positively related to new product program performance under specific conditions such as when strategic consensus among managers is high. On the other hand, the positive effect of proactive MO on new product program performance is further strengthened when learning orientation and marketing power are high. Overall, this study suggests that the effects of responsive and proactive MO on new product program performance are more complex than previously theoretically argued and empirically examined.     

Feature selection methods involving support vector machines for prediction of insolvency in non‐life insurance companies

We propose two novel approaches for feature selection and ranking tasks based on simulated annealing (SA) and Walsh analysis, which use a support vector machine as an underlying classifier. These approaches are inspired by one of the key problems in the insurance sector: predicting the insolvency of a non‐life insurance company. This prediction is based on accounting ratios, which measure the health of the companies. The approaches proposed provide a set of ratios (the SA approach) and a ranking of the ratios (the Walsh analysis ranking) that would allow a decision about the financial state of each company studied. The proposed feature selection methods are applied to the prediction the insolvency of several Spanish non‐life insurance companies, yielding state‐of‐the‐art results in the tests performed. Copyright © 2005 John Wiley & Sons, Ltd.     

Artificial neural networks with feature transformation based on domain knowledge for the prediction of stock index futures

A feature transformation method based on domain knowledge for arti?cial neural networks (ANNs) is proposed. The method of feature transformation based on domain knowledge converts continuous values into discrete values in accordance with the knowledge of experts in speci?c application domains. This approach effectively ?lters data, trains the classi?er, and extracts the rules from the classi?er. In addition, it reduces the dimensionality of the feature space, which not only decreases the cost and time in the operation but also enhances the generalizability of the classi?er. The experimental results of the proposed approach will be compared and tested statistically with the results of the linear transformation method. The results show that the method of feature transformation based on domain knowledge outperforms the linear transformation in modelling of ANNs. Copyright © 2004 John Wiley & Sons, Ltd.     

The service sector of Cuba: An analysis and critique

The Cuban economy has experienced a significant increase in foreign direct investment over the past decade due to the end of the Soviet Union and the establishment of more liberal trade policies. Although the U.S. embargo of trade with Cuba still exists, there has been movement lately that suggests the end of the four‐decades‐old policy. The purpose of this article is to analyze the current Cuban business environment and to identify potential entrepreneurial opportunities in the service sector. Major trade partners are also reviewed and managerial implications discussed. © 2006 Wiley Periodicals, Inc.     

Measuring Hospital Performance in the Presence of Quasi-Fixed Inputs: An Analysis of Québec Hospitals

This research proposes an approach to measure hospital performance based on a generalization of Banker and Morey (1986) and Førsund (1996). This approach considers quasi-fixed inputs explicitly, calculates their implicit cost, and quantifies returns to scale. The performance measure is decomposed into allocative and technical inefficiencies. Based on a very complete data set of Québec hospitals, we find that significant inefficiencies of up to 17% ($700 CAN million) could have been saved through improved performance. Postestimation analyses that include qualitative measures of care suggest that differences in performance are attributable to differences in management or unobservable quality of care rather than patient case mix.     

Information Matrices for Non Full Rank Subsystems

Consider the standard linear model Y=X θ + ε. If the parameter of interest is a full rank subsystem K′θ of mean parameters, the associated information matrix can be defined via an extremal representation. For rank deficient subsystems, Pukelsheim (1993) introduced the notion of generalized information matrices that inherit many properties of the information matrices. However, this notion is not a direct extension of the full rank case in the sense that the definition of the generalized information matrix applied to full rank subsystems does not lead to the usual information matrix. In this paper, we propose a definition of the information matrix via an extremal representation that encompasses the full rank and the non-full rank cases. We also study its properties and show its links with the generalized information matrices.     

Asymmetric ACD models: Introducing price information in ACD models

This paper proposes an asymmetric autoregressive conditional duration (ACD) model, which extends the ACD model of Engle and Russell (1998). The asymmetry consists of letting the duration process depend on the state of the price process. If the price has increased, the parameters of the ACD model can differ from what they are if the price has decreased. The model is applied to the bid-ask quotes of two stocks traded on the NYSE and the evidence in favour of asymmetry is strong. Information effects (Easley and O'Hara 1992) are also empirically relevant. As the model is a transition model for the price process, it delivers `market forecasts' of where prices are heading. A trading strategy based on the model is implemented using tick-by-tick data.While remaining responsible for any error in this paper, the authors would like to thank R. Anderson, G. Le Fol, C. Gouriéroux, J. Jasiak, W. Pohlmeier, A. Roell, O. Scaillet, S. Wei and three anonymous referees for useful remarks and suggestions on previous versions. The authors would also like to thank A. Ruttiens from KBC-CBC for useful discussions on practical issues related to trading. Support of the European Commission Human Capital and Mobility Program through the network `Econometric inference using simulation methods' is gratefully acknowledged. This paper presents research results of the Belgian Program on Interuniversity Poles of Attraction initiated by the Belgian State, Prime Minister's Office, Science Policy Programming. The scientific responsibility is assumed by the authors.     

This is what the leading indicators lead

We propose an optimal filter to transform the Conference Board Composite Leading Index (CLI) into recession probabilities in the US economy. We also analyse the CLI's accuracy at anticipating US output growth. We compare the predictive performance of linear, VAR extensions of smooth transition regression and switching regimes, probit, non‐parametric models and conclude that a combination of the switching regimes and non‐parametric forecasts is the best strategy at predicting both the NBER business cycle schedule and GDP growth. This confirms the usefulness of CLI, even in a real‐time analysis. Copyright © 2002 John Wiley & Sons, Ltd.