Does ignoring multidestination trips in the travel cost method cause a systematic bias?

The present paper demonstrates that treating multidestination trips (MDT) as single-destination trips does not involve any systematic upward or downward bias in consumer surplus (CS) estimates because the direct negative effect of a price increase (treating MDT as a single-destination trip) is offset by a shift in the estimated demand curve. Still, ignoring MDT can greatly underestimate or overestimate the CS. In addition, we demonstrate that there is a sound theoretical basis for using preference information for allocating travel costs between different sites included in the MDT package. A novel extreme value approach is proposed, which does not require any overly restrictive assumptions about consumer preferences. This approach is applied to the zonal travel cost model of the Bellenden Ker National Park, Australia. Parametric and non-parametric estimation techniques are used for calculating CS estimates, and the effects of different MDT treatments and estimation methods are compared.     

New challenges for futures studies

Challenges coming from futures studies to other disciplines because of the rapidity and inter-relatedness of changes for which no discipline on its own can face the different correlated and global challenges, will be examined mainly in relation to social sciences. social sciences on the other hand reflect the need to overcome fragmentation within each discipline and between the various social sciences, in an effort at least of interdisciplinarity to face the growing uncertainty in decision making at every level: local, national and international. Social sciences are also slowly realising that a future oriented perspective is needed to empower analysis and actually reflect society in its continuous dynamicity. Citizens need the possibility to live within the rapidity of changes in the Information Society through the availability of futures studies in different forms as well as social analysis that is dynamic and interdisciplinary. The special link between society and ecological issues in a future oriented perspective will be the specific area to express the relevancy of the correlation between futures studies and social sciences.     

Lead time reduction through ICT application in the footwear industry: A case study

This paper analyses some relevant supply chain management issues for Italian firms operating in the footwear industry. The analysis is carried out on representative firms, located in a specialized regional district with a high density of shoe manufacturing companies. Companies were experiencing substantial problems in the management of supplier relationships, as well as in the commercial/distributive channel. Specifically, synchronization issues in the logistics pipeline were weakening firms’ lead time performances.The case study presented strives to highlight the critical points in the set up of the supply chain management programme, as well as the main results obtained. The case study also shows that the adoption of tailored Information and Communication Technology (ICT) tools has the potential to save significant lead time in supplier/buyer relationships. From the case study, generalities can be drawn and transferred to the footwear industry.     

Machine learning advances for time series forecasting

In this paper, we survey the most recent advances in supervised machine learning (ML) and high-dimensional models for time-series forecasting. We consider both linear and nonlinear alternatives. Among the linear methods, we pay special attention to penalized regressions and ensemble of models. The nonlinear methods considered in the paper include shallow and deep neural networks, in their feedforward and recurrent versions, and tree-based methods, such as random forests and boosted trees. We also consider ensemble and hybrid models by combining ingredients from different alternatives. Tests for superior predictive ability are briefly reviewed. Finally, we discuss application of ML in economics and finance and provide an illustration with high-frequency financial data.