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This paper investigates the temporal inflow profile that minimises the total cost of travel for a single route. The problem is formulated to consider the case in which the total demand to be serviced is fixed. The approach used here is a direct calculation of the first order variation of total system cost with respect to variations in the inflow profile. Two traffic models are considered; the bottleneck with deterministic queue and the kinematic wave model. For the bottleneck model a known solution is recovered. The wave model proves more difficult and after eliminating the possibility of a smooth inflow profile the restricted case of constant inflow is solved. As the space of possible profiles is finite dimensional in this case, the standard techniques of calculus apply. We establish a pair of equations that are satisfied simultaneously by the optimal inflow and time of first departure.

In this chapter, the author considers a three-sector general equilibrium model in the context of a developing nation to find out the impact of an increase in foreign capital inflow on the welfare level of the nation. Comparative static analysis reveals that an increase in the inflow of foreign capital causes redistribution across the factors of production and a reallocation of resources, reflected through the change in output. Moreover, the author considers the case of technology transfer and proves that an increase in foreign capital inflow makes the country better off in terms of social welfare even if the foreign capital is fully repatriated. Hence, this work shows that in the absence of any trade distortion, a partial investment liberalisation causes a welfare gain for a small open economy.

Longitudinal data are required to characterise and measure the dynamics of income-related health inequalities (IRHI). This chapter develops a framework to evaluate the impact of population changes on the level of cross-sectional IRHI over time and thereby provides further insight into how health inequalities develop or perpetuate themselves in a society. The approach is illustrated by an empirical analysis of the increase in IRHI in Great Britain between 1999 and 2004 using the British Household Panel Survey. The results imply that levels of IRHI would have been even higher in 2004 but for the entry of youths into the adult population and deaths, with these natural processes of population turnover serving to partially mask the increase in IRHI among the resident adult population over the five-year period. We conclude that a failure to take demographic changes into account may lead to erroneous conclusions on the effectiveness of policies designed to tackle health inequalities.

In the last years, the design and implementation of web-based education systems has grown exponentially, spurred by the fact that neither students nor teachers are bound to a specific location and that this form of computer-based education is virtually independent of any specific hardware platform. These systems accumulate a large amount of data: educational data mining and learning analytics are the two much related fields of research with the aim of using these educational data to improve the learning process. In this chapter, the authors investigate the peer assessment setting in communities of learners. Peer assessment is an effective didactic strategy, useful to evaluate groups of students in educational environments such as high schools and universities where students are required to answer open-ended questions to increase their problem-solving skills. Furthermore, such an approach could become necessary in the learning contexts where the number of students to evaluate could be very large as, for example, in massive open online courses. Here the author focus on the automated support to grading open answers via a peer evaluation-based approach, which is mediated by the (partial) grading work of the teacher, and produces a (partial as well) automated grading. The author propose to support such automated grading by means of two methods, coming from the data-mining field, such as Bayesian Networks and K-Nearest Neighbours (K-NN), presenting some experimental results, which support our choices.

The authors propose a family of tests for stationarity against a local unit root. It builds on the Karhunen–Loève (KL) expansions of the limiting CUSUM process under the null hypothesis and a local alternative. The variance ratio type statistic VRq is a ratio of quadratic forms of q weighted Gaussian sums such that the nuisance long-run variance cancels asymptotically without having to be estimated. Asymptotic critical values and local power functions can be calculated by standard numerical means, and power grows with q. However, Monte Carlo experiments show that q may not be too large in finite samples to obtain tests with correct size under the null. Balancing size and power results in a superior performance compared to the classic KPSS test.

Many consumer choice situations are characterized by the simultaneous demand for multiple alternatives that are imperfect substitutes for one another. A simple and parsimonious multiple discrete-continuous extreme value (MDCEV) econometric approach to handle such multiple discreteness was formulated by Bhat (2005) within the broader Kuhn–Tucker (KT) multiple discrete-continuous economic consumer demand model of Wales and Woodland (1983). In this chapter, the focus is on presenting the basic MDCEV model structure, discussing its estimation and use in prediction, formulating extensions of the basic MDCEV structure, and presenting applications of the model. The paper examines several issues associated with the MDCEV model and other extant KT multiple discrete-continuous models. Specifically, the paper discusses the utility function form that enables clarity in the role of each parameter in the utility specification, presents identification considerations associated with both the utility functional form as well as the stochastic nature of the utility specification, extends the MDCEV model to the case of price variation across goods and to general error covariance structures, discusses the relationship between earlier KT-based multiple discrete-continuous models, and illustrates the many technical nuances and identification considerations of the multiple discrete-continuous model structure. Finally, we discuss the many applications of MDCEV model and its extensions in various fields.