THEORY AND PRACTICE OF GVAR MODELLING

The Global Vector Autoregressive (GVAR) approach has proven to be a very useful approach to analyse interactions in the global macroeconomy and other data networks where both the cross‐section and the time dimensions are large. This paper surveys the latest developments in the GVAR modelling, examining both the theoretical foundations of the approach and its numerous empirical applications. We provide a synthesis of existing literature and highlight areas for future research.     

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.     

Validity of sustainability framework for China’s mining cities – a structural equation modelling approach

Many studies developed their framework for sustainability through indicator systems, but the interactions and relationships within these indicators have not been studied yet. In this article, based on indicator systems, we use a sample of 78 mining cities in China and employ structural equation modelling (SEM) method to explore the validity of sustainability framework. Our empirical results show that resources abundance positively affects urbanization level significantly but negatively affects cleaner production level and environmental protection level significantly; economic strength positively affects urbanization level significantly; cleaner production level has a significant effect on economic strength and non-mineral resources conversion efficiency. We also find that when economic strength is low, resources abundance might weaken the positive effect of economy on urbanization.     

The determinants of household recycling: social influence,public policies and environmental preferences

Our article aims at understanding the determinants of households’ selective waste-sorting behaviours, based on data from an original survey of 694 individuals in the French Provence–Alpes–Côte d’Azur region. The applied literature focuses mainly on countries with high recycling rates. We focus on a region with the lowest recycling rate in France, a country that recycles less than the European country average. We first apply polychoric principal components analysis to reduce the number of explanatory variables to a set of six factors. In a second step, we use a probit model to estimate the probability of waste sorting as a function of these factors. This model tests several hypotheses emerging from the recent literature on behavioural economics applied to households’ selective sorting. This literature pays particular attention to the social influence on recycling behaviour, which has been studied mostly by sociologists and psychologists. The results of our empirical analysis confirm some of the findings in the literature. However, they also highlight some unique features, such as social influences having a negative impact on recycling. This finding contrasts with most of the literature, which finds a positive relationship of social influence on pro-environmental behaviour.     

Measuring Efficiency: A Comparison of Multilevel Modelling and Data Envelopment Analysis in the Context of Higher Education

Data envelopment analysis (DEA) and multilevel modelling (MLM) are applied to a data set of 54,564 graduates from UK universities in 1993 to assess whether the choice of technique affects the measurement of universities’ performance. A methodology developed by Thanassoulis and Portela (2002; Education Economics, 10(2), pp. 183–207) allows each individual's DEA efficiency score to be decomposed into two components: one attributable to the university at which the student studied and the other attributable to the individual student. From the former component, a measure of each institution's teaching efficiency is derived and compared to the university effects from various multilevel models. The comparisons are made within four broad subjects: pure science, applied science, social science and arts. The results show that the rankings of universities derived from the DEA efficiencies which measure the universities’ own performance (i.e., having excluded the efforts of the individuals) are not strongly correlated with the university rankings derived from the university effects of the multilevel models. The data were also used to perform a university‐level DEA. The university efficiency scores derived from these DEAs are largely unrelated to the scores from the individual‐level DEAs, confirming a result from a smaller data set (Johnes, 2006a; European Journal of Operational Research, forthcoming). However, the university‐level DEAs provide efficiency scores which are generally strongly related to the university effects of the multilevel models.     

Comparing debt characteristics and LGD models for different collections policies

This paper discusses the similarities and differences in the collection process between in-house and 3rd party collection. The objective is to show that, although the same type of modelling approach to estimating the Loss Given Default (LGD) can be used in both cases, the details will be significantly different. In particular, the form of the LGD distribution suggests that one needs to split the distribution in different ways in the two cases, as well as using different variables. The comparisons are made using two data sets of the collection outcomes from two sets of unsecured consumer defaulters.     

A note on the ecological-economic modelling of marine reserves in fisheries

This paper gives an overview of the bioeconomic modelling of marine reserves, and illustrates how economists have responded to the modelling results found in the ecological literature. The economic analysis is shown to be far more pessimistic with regards to the potential of marine reserves as a fisheries management tool, than what one finds in the purely ecological analysis, the reason being the latter's neglect of issues such as discounting and economic incentive behaviour. However, economic analysis, despite some of it being relatively advanced with regards to spatiality, is still simplistic with regards to for instance ecosystem and habitat content. A simple expansion of the existing bioeconomic models with regards to positive habitat effects of area closures is presented and analysed, showing room for improved results from marine reserve implementation as compared to the existing analysis.     

A comparison of regret-based and utility-based discrete choice modelling – an empirical illustration with hospital bed choice

There is some concern that the unobserved preference heterogeneity in random utility maximization theory-based discrete choice experiment modelling is an important source of error variability. The randomness in utility is often interpreted as interpersonal preference heterogeneity but it can also be intrapersonal random variation in preferences. We compare utility maximization and regret minimization-based choice models’ sensitivity to individual heterogeneity, examine differences between two consecrated models and validate with empirical illustrations. We use frequency of category (public, semi-private, and private) of bed chosen from Swiss cross-sectional datasets (2007–2012) to compare two approaches – utility maximization and regret minimization by applying multinomial logit (MNL) models in regard to the variances in utility (regret) function, goodness-of-fit and predicted marginal effects (pseudo-elasticity) of additional payment. We find parameters with the same sign and estimates with almost same order of magnitude in both the approaches. The statistical significance of attribute effects is consistent in all variants of utility -based MNL models while effects of different attributes are significant only in heteroskedastic extreme value (HEV) variant of regret-based MNL models. This empirical illustration suggests that HEV variant of regret-based models perform better in capturing attribute effects in choice behaviour.     

Position-Dependent order effects on the prediction of consumer preferences in repeated choice experiments

Using stated choice data collected by experimental design with repeated choice tasks, this study developed an approach to quantify the position-dependent order effects on the prediction of preferences and marginal willingness to pay for product attributes. Results showed that repeated choice tasks allow learning to occur. Models with order effect adjustments showed significant improvements in goodness of fit. Attribute-specific polynomial trends showed the best fit among all models, which could possibly be explained by respondents’ familiarity and sensitivity to different product attributes. Repeated-choice experiments have a good potential to capture consumer preferences more accurately than the single-choice design. But order effects need to be taken into account for preferences and market prediction.