Nonlinear budget constraints and consumer demand: An application to public programs for residential housing

This study presents a methodology for exploiting the nonlinear hedonic nature of housing prices to estimate the compensated demands of households for particular housing attributes. The methodology is employed to provide Hicksian benefit measures of a particular housing subsidy program typical of those undertaken recently in developing countries.     

The reliability of constructs derived from attitude-behavior theories: an application of generalizability theory in the health sector

Theories of reasoned action, interpersonal behavior and self-efficacy have been frequently used for research into health-related behavior. The present study investigated two methods for assessing the reliability of their theories' constructs: the test-retest method and generalizability theory. Firstly, it is demonstrated with a numerical example that the traditional test-retest coefficient has an anatomical link with the generalizability coefficient. Secondly, generalizability theory was applied in order to identify the number of occasions each construct must be measured to assure its reliability. This procedure was applied to the data collected in a study investigating the psychosocial factors influencing exercising and smoking behaviors among pregnant women. Measuring the constructs of direct attitude, habit, and intention on only one occasion was sufficient to ensure high levels of reliability. Moreover, reliability of the other constructs would be ensured if they were measured on more than one occasion.     

Counterfactual distributions with sample selection adjustments: Econometric theory and an application to the Netherlands

Several recent papers use the quantile regression decomposition method of Machado and Mata [Machado, J.A.F. and Mata, J. (2005). Counterfactual decomposition of changes in wage distributions using quantile regression, Journal of Applied Econometrics, 20, 445–65.] to analyze the gender gap across log wage distributions. In this paper, we prove that this procedure yields consistent and asymptotically normal estimates of the quantiles of the counterfactual distribution that it is designed to simulate. Since employment rates often differ substantially by gender, sample selection is potentially a serious issue for such studies. To address this issue, we extend the Machado–Mata technique to account for selection. We illustrate our approach to adjusting for sample selection by analyzing the gender log wage gap for full-time workers in the Netherlands.     

Remaining life estimation of used components in consumer products: Life cycle data analysis by Weibull and artificial neural networks

Environmental awareness and legislative pressures have made manufacturers responsible for the take-back and end-of-life treatment of their products. To competitively exploit these products, one option is to incorporate used components in “new” or remanufactured products. However, this option is partly limited by a firm's ability to assess the reliability of used components. A comprehensive two-step approach is proposed. The first stage phase statistically analyzes the behavior of components for reuse. A well-known reliability assessment method, the Weibull analysis, is applied to the time-to-failure data to assess the mean life of components. In the second phase, the degradation and condition monitoring data are analyzed by developing an artificial neural network (ANN) model. The advantages of this approach over traditional approaches employing multiple regression analysis are highlighted with empirical data from a consumer product. Finally, the Weibull analysis and the ANN model are then integrated to assess the remaining useful life of components for reuse. This is a critical advance in sustainable management of supply chains since it allows for a better understanding of not only service requirements of product, but the remaining life in a product and hence its suitability for reuse or remanufacture. Future work should assess: (1) reduction in downtime of process equipment through the implementation of this technique as a means to better manage preventative maintenance; (2) reduce field failure of remanufactured product; (3) selling-service strategy through implementation of the proposed methodology.     

Entrepreneurial intent in a transitional economy: an application of the theory of planned behavior to Romanian students

This study examines entrepreneurial intent in Romanian students using the Theory of Planned Behavior (Ajzen, Adv. Exp. Soc. Psychol., 20:1–63, 1987). Using multiple regression analyses to test for mediation, we analyzed survey data from 324 Romanian students. Some of our findings were consistent with the Theory of Planned Behavior, while others were not. As expected, we found that self-efficacy and the desirabilities associated with creating a venture were positively related to entrepreneurial intent. However, surprisingly, we found that the more supportive the students’ referents were, the less likely the students were to have intentions of starting a business.     

Classification and aggregation: An application to industrial classification in cps data

In this paper we offer a method for deciding how to aggregate a set of elementary industries. The method is then applied to the problem of estimating a wage equation that allows for industry-specific effects. Our method explicitly formalizes the trade-off between goodness-of-fit and parsimony implicit in an aggregation problem. By varying the parameter of the assumed loss function, one obtains a whole sequence of aggregation levels. Further, the resulting sequence is consistent; that is, groupings formed at one level of aggregation will never be undone when one aggregates further.     

On random fuzzy variables of second order and their application to linear statistical inference with fuzzy data

This paper summarizes some results on random fuzzy variables with existing expectation and variance, called random fuzzy variables of second order. Using the Frechét-principle and – via support functions – the embedding of convex fuzzy sets into a Banach space of functions it especially presents a unified view on expectation and variance of random fuzzy variables. These notions are applied in developing linear statistical inference with fuzzy data. Detailed investigations are presented concerning best linear unbiased estimation in linear regression models with fuzzy observations. Received: November 1999     

The collapsibility theorem in log-linear analysis of categorical data: an application in program evaluation

The collapsibility theorem describes both the circumstances in which the effects of hierarchical models change when additional variables are introduced, as the circumstances in which the exclusion of certain variables and the analysis of specific marginal tables may lead to different conclusions. The partial association model is here considered as a specific example of three-dimensional log-linear analysis. Collapsibility is examined in an empirical study currently being performed in Catalonia with regard to program evaluation in penitentiary centers.     

Accounting for risk in productivity analysis: an application to Norwegian dairy farming

Empirical studies have often shown wide differences in productivity among firms. Although several studies have sought to identify factors causing such differences, only a few studies have examined the effects of risk and risk aversion on productivity. In this study, using Norwegian dairy farming data for 2009, we examined the effects of different aspects of risk on productivity. We used a range of variables to construct indices of risk taking, risk perception and risk management. These indices were then included as arguments in an input distance function which represents the production technology. Our results show that these risk indices did affect productivity. Regional differences in productivity, though small, were also found to exist, suggesting that unobserved edaphic factors that differ between regions also affected productivity.     

Specification tests based on the heterogeneous generalized gamma model of duration: With an application to Kennan's strike data

In this paper, tests for neglected heterogeneity and functional form misspecification of some commonly used parametric distributions are derived within a heterogeneous generalized gamma model. It is argued that the conventional test of heterogeneity may not be valid when the underlying hazard function is misspecified. Hence, if the estimated hazard function is deemed restrictive, tests for functional form misspecification should accompany any test of heterogeneity. An empirical illustration based on Kennan's (1985) model of strikes is used to show that incorrect inferences may be drawn, as in a number of previous analyses, if the relevant restrictions are not tested jointly.     

Comparing supervised learning algorithms and artificial neural networks for conflict prediction: performance and applicability of deep learning in the field

Quality & Quantity - Machine learning algorithms and artificial neural networks promise a new and powerful approach for making better and more transferable predictions in global conflict...     

On the shape of posterior densities and credible sets in instrumental variable regression models with reduced rank: An application of flexible sampling methods using neural networks

Likelihoods and posteriors of instrumental variable (IV) regression models with strong endogeneity and/or weak instruments may exhibit rather non-elliptical contours in the parameter space. This may seriously affect inference based on Bayesian credible sets. When approximating posterior probabilities and marginal densities using Monte Carlo integration methods like importance sampling or Markov chain Monte Carlo procedures the speed of the algorithm and the quality of the results greatly depend on the choice of the importance or candidate density. Such a density has to be ‘close’ to the target density in order to yield accurate results with numerically efficient sampling. For this purpose we introduce neural networks which seem to be natural importance or candidate densities, as they have a universal approximation property and are easy to sample from. A key step in the proposed class of methods is the construction of a neural network that approximates the target density. The methods are tested on a set of illustrative IV regression models. The results indicate the possible usefulness of the neural network approach.     

Estimation of trade and domestic distortions: an application to world agriculture

The world’s nations often produce commodities for which they have no apparent comparative advantage, and do so with techniques that are not particularly efficient by world standards. These inefficiencies may arise from various forms of trade and domestic distortions, as described in Chau et al., Int Econ Rev 44:1079–1095, (2003). We estimate these distortions for 33 countries of the world using a newly compiled data set. We find that domestic distortions tend to be slightly more important than trade distortions. For the average country, revenues in the agricultural sector would be 26% higher if domestic distortions were eliminated, but 21% higher if trade distortions were eliminated. Our measures of trade and domestic distortions across countries provide a complement to measures of protectionism such as producer subsidy equivalents.     

Classification of various attacks and their defence mechanism in online social networks: a survey

Due to the popularity and user friendliness of the Internet, numbers of users of online social networks (OSNs) and social media have grown significantly. However, globally utilised, social networks are the consequence of the lack of understanding of secrecy and protection on OSN and media has increased. Secrecy and surety of OSNs need to be inquired from various positions. According to recent studies, OSN users expose their private information such as email address, phone number etc. In this paper, we have presented a high-level classification of recent OSN attacks for recognising the problem and analysing the blow of such attacks on World Wide Web. We have also discussed OSN attacks on different social networking web applications by citing certain recent reports such as Kaspersky security network and Sophos security threat report. We also offer some simple-to-implement user practice tips to protect the system and user’s information. In addition to this, we have discussed a comprehensive analysis of numerous defensive approaches on OSN security. Lastly, based on the acknowledged strength and faults of these defensive approaches, we have explained open research issues.     

Frontier production functions,technical efficiency and panel data: With application to paddy farmers in India

Frontier production functions are important for the prediction of technical efficiencies of individual firms in an industry. A stochastic frontier production function model for panel data is presented, for which the firm effects are an exponential function of time. The best predictor for the technical efficiency of an individual firm at a particular time period is presented for this time-varying model. An empirical example is presented using agricultural data for paddy farmers in a village in India.This article is a revision of the Invited Paper presented by the senior author in the Productivity and Efficiency Analysis sessions at the ORSA/TIMS 30th Joint National Meeting, Philadelphia, Pennsylvania, 29–31 October 1990. We have appreciated comments from Martin Beck, Phil Dawson, Knox Lovell and three anonymous referees. We gratefully acknowledge the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) for making available to us the data obtained from the Village Level Studies in India.     

县域城镇化水平差异的人工神经网络测定——以陕西省为例

在已有研究的基础上,运用人工神经网络的理论和方法,构建了BP神经网络模型,并对2008年陕西省83个县的县域城镇化水平进行了测度。结果表明:陕西省县域城镇化水平存在显著分异,评价结果与专家的判断基本一致。陕北、关中地区县域城镇化水平较高,陕南则相对较低,各自区内差异明显。可见,BP神经网络模型运用于县域城镇化水平简单、实用,且有效避免了人工赋权的主观性,具有良好的应用前景。     

Dose–response modelling for bivariate covariates with and without a spike at zero: theory and application to binary outcomes

In epidemiology and clinical research, there is often a proportion of unexposed individuals resulting in zero values of exposure, meaning that some individuals are not exposed and those exposed have some continuous distribution. Examples are smoking or alcohol consumption. We will call these variables with a spike at zero (SAZ). In this paper, we performed a systematic investigation on how to model covariates with a SAZ and derived theoretical odds ratio functions for selected bivariate distributions. We consider the bivariate normal and bivariate log normal distribution with a SAZ. Both confounding and effect modification can be elegantly described by formalizing the covariance matrix given the binary outcome variable Y. To model the effect of these variables, we use a procedure based on fractional polynomials first introduced by Royston and Altman (1994, Applied Statistics 43: 429–467) and modified for the SAZ situation (Royston and Sauerbrei, 2008, Multivariable model‐building: a pragmatic approach to regression analysis based on fractional polynomials for modelling continuous variables, Wiley; Becher et al., 2012, Biometrical Journal 54: 686–700). We aim to contribute to theory, practical procedures and application in epidemiology and clinical research to derive multivariable models for variables with a SAZ. As an example, we use data from a case–control study on lung cancer.