High-speed rail and regional development: the case of intermediate stations

High-speed rail has developed both nationally and internationally in Europe as a successful alternative to both air and road over distances of 400–600 km. Inter-city traffic, especially between the major metropolitan areas in North-west Europe has benefitted greatly from the investment in this network. This paper explores two issues: the impact on the intermediate areas between these major metropolitan areas and the creation of potential cross-border inter-regional services. The evidence shows how both levels of service and potential economic impacts have been much less pronounced in these intermediate areas. Such areas have been affected both by a failure to see greatly improved direct access to major cities other than within their own countries and a lack of new cross-border inter-regional services. The paper argues that the creation of the high-speed rail TEN-T has not met the primary objectives of reducing regional disparities in accessibility or reducing the effect of national borders on regional integration. To achieve this requires not just infrastructure provision but an appropriate regulatory framework for service provision and accompanying measures at the local level.     

A model for planning locations of temporary distribution facilities for emergency response

We propose a network flow model for dynamic selection of temporary distribution facilities and allocation of resources for emergency response planning. The model analyzes the transfer of excess resources between temporary facilities operating in different time periods in order to reduce deprivation. Numerical analysis shows that the location of temporary facilities is determined by the demand and supply points. This work contributes to the emergency response planning that requires a quick response for the supply of relief materials immediately after a disaster hits a particular area.     

Modelling property values in Nigeria using artificial neural network

Unreliable and inaccurate property valuation has been associated with techniques currently used in property valuation. A possible explanation for these findings may be due to the utilisation of traditional valuation methods. In the current study, an artificial neural network (ANN) is applied in property valuation using the Lagos metropolis property market as a representative case. Property sales transactions data (11 property attributes and property value) were collected from registered real estate firms operating in Lagos, Nigeria. The result shows that the ANN model possesses a good predictive ability, implying that it is suitable and reliable for property valuation. The relative importance analysis conducted on the property attributes revealed that the number of servants’ quarters is the most important attribute affecting property values. The findings suggest that the ANN model could be used as a tool by real estate stakeholders, especially valuers and researchers for property valuation.     

Theoretical,empirical, and operational models in hotel location research

This paper aims to review past literature on hotel location models and evaluate the state of the art, as well as set out future directions. This study divides hotel location models into three major categories: theoretical models, empirical models, and operational models. Four theoretical hotel location models are reviewed and discussed, including the tourist-historic city model, the mono-centric model, the agglomeration model, and the multi-dimensional model. Based on previous literature, six empirical models and three operational models of hotel location are elaborated. Furthermore, some challenges related to hotel location studies are discussed, and future research directions are provided. In particular, we advocate the development of more sophisticated hotel location models and the use of Geographic Information System (GIS) in hotel location analysis.     

Are the BRIC countries overtaking intermediate countries in the race for international R&D? The case of Spain

The emergence of Brazil, Russia, India and China (BRIC) as international locations for foreign direct investment in R&D is a sign that multinationals are relocating their technological activities to new territories. This trend may weaken the supremacy of the developed countries until now considered leaders in innovation, and may mean the loss of the competitive advantages enjoyed by the countries considered intermediate innovators. This paper examines the situation of Spain as a typical intermediate economy and compares it to its main competitors among the BRICs. Based on eight case studies of subsidiaries with R&D centres in Spain, we conclude that the policies adopted by certain emerging economies to develop their national innovation systems are proving effective and that these countries now pose a threat to intermediate economies. However, the BRICs still lag behind in terms of the security of their institutional framework; this situation leaves intermediate countries in an advantageous position.     

Promoting cost efficiency and uniformity in parcel delivery centre locations and service areas: a GIS-based analysis

This paper provides a systematic framework to determine local delivery centre (LDC) locations and service areas to reduce delivery costs and balance the allocation of parcel delivery loads to promote sustainable LDC development. Based on a case study of a parcel distribution company in Korea, this study proposes two strategies for LDC service area delineation within each terminal service boundary that either minimises the total delivery time or balances the allocation of delivery loads. This research contributes to improving cost efficiency and balancing delivery load allocations in planning LDC locations and service areas that can potentially promote the long-term mutual sustainable development of parcel distribution companies and their local delivery counterparts.     

Cointegration as an explanation for the Meese–Rogoff puzzle

Some economists suggest that the failure of exchange-rate models to outperform the random walk in exchange rate forecasting out of sample can be attributed to failure to take into account cointegration when it is present. We attempt to find out if cointegration matters for forecasting accuracy by examining the relation between the stationarity and size of the forecasting error. Results based on three macroeconomic models of exchange rates do not provide strong support for the proposition that cointegration matters for forecasting accuracy. The simulation results show that while stationary errors tend to be smaller than non-stationary errors, this is not a universal rule. Irrespective of the presence or absence of cointegration, none of the three models can outperform the random walk in out-of-sample forecasting, which means that cointegration cannot solve the Meese–Rogoff puzzle.     

Optimizing station location and fleet composition for a high-speed rail line

This paper proposes a new strategic planning model for high-speed rail ventures. It is a mixed-integer optimization model that applies to a given line and focuses on two key strategic decisions: station location and fleet composition. Our purpose is to improve on previous station location models by including fleet composition decisions. In the new model, we additionally take into account in an approximate fashion the interrelationships between strategic and subsequent tactical decisions, regarding line planning, train scheduling and fleet assignment issues. The usefulness of the model is demonstrated for a case study involving a planned Lisbon-Oporto high-speed rail line.     

Comparing linear and nonlinear forecasts for stock returns

We compare the out-of-sample performance of monthly returns forecasts for two indices, namely the Dow Jones (DJ) and the Financial Times (FT) indices. A linear and a nonlinear artificial neural network (ANN) model are used to generate the out-of-sample competing forecasts for monthly returns. Stationary transformations of dividends and trading volume are considered as fundamental explanatory variables in the linear model and the input variables in the ANN model. The comparison of out-of-sample forecasts is done on the basis of forecast accuracy, using the Diebold and Mariano test [J. Bus. Econ. Stat. 13 (1995) 253.], and forecast encompassing, using the Clements and Hendry approach [J. Forecast. 5 (1998) 559.]. The results suggest that the out-of-sample ANN forecasts are significantly more accurate than linear forecasts of both indices. Furthermore, the ANN forecasts can explain the forecast errors of the linear model for both indices, while the linear model cannot explain the forecast errors of the ANN in either of the two indices. Overall, the results indicate that the inclusion of nonlinear terms in the relation between stock returns and fundamentals is important in out-of-sample forecasting. This conclusion is consistent with the view that the relation between stock returns and fundamentals is nonlinear.     

Sequential estimation of a location parameter and powers of a scale parameter from delayed observations

The problem of sequentially estimating a location parameter and powers of a scale parameter is considered in the case when the observations become available at random times. Certain classes of sequential estimation procedures are derived under an invariant balanced loss function and with the observation cost determined by a convex function of the stopping time and the number of observations up to that time.