A Graphical Tool for Interpreting Regression Coefficients of Trinomial Logit Models

Multinomial logit (also termed multi-logit) models permit the analysis of the statistical relation between a categorical response variable and a set of explicative variables (called covariates or regressors). Although multinomial logit is widely used in both the social and economic sciences, the interpretation of regression coefficients may be tricky, as the effect of covariates on the probability distribution of the response variable is nonconstant and difficult to quantify. The ternary plots illustrated in this article aim at facilitating the interpretation of regression coefficients and permit the effect of covariates (either singularly or jointly considered) on the probability distribution of the dependent variable to be quantified. Ternary plots can be drawn both for ordered and for unordered categorical dependent variables, when the number of possible outcomes equals three (trinomial response variable); these plots allow not only to represent the covariate effects over the whole parameter space of the dependent variable but also to compare the covariate effects of any given individual profile. The method is illustrated and discussed through analysis of a dataset concerning the transition of master’s graduates of the University of Trento (Italy) from university to employment.     

Bayesian analysis of discrete time warranty data

Summary.  The analysis of warranty claim data, and their use for prediction, has been a topic of active research in recent years. Field data comprising numbers of units returned under guarantee are examined, covering both situations in which the ages of the failed units are known and in which they are not. The latter case poses particular computational problems for likelihood-based methods because of the large number of feasible failure patterns that must be included as contributions to the likelihood function. For prediction of future warranty exposure, which is of central concern to the manufacturer, the Bayesian approach is adopted. For this, Markov chain Monte Carlo methodology is developed.     

On calculating the fisher information in order statistics

This article gives a simple result for the expression of the Fisher information in order statistics. This result enables us to calculate easily the Fisher information in any set of order statistics whose details have been known to be messy and complicated. We consider here its application in the optimal spacing problem where the exact Fisher information in order statistics has been approximated with the asymptotic information or the reciprocal of the variance of a suitable estimator. This work was supported by Korea Research Foundation Grant(KRF-2000-015-DP0056)     

Assessment of modeling longitudinal binary data based on graphical methods

Longitudinal categorical data are commonly applied in a variety of fields and are frequently analyzed by generalized estimating equation (GEE) method. Prior to making further inference based on the GEE model, the assessment of model fit is crucial. Graphical techniques have long been in widespread use for assessing the model adequacy. We develop alternative graphical approaches utilizing plots of marginal model-checking condition and local mean deviance to assess the GEE model with logit link for longitudinal binary responses. The applications of the proposed procedures are illustrated through two longitudinal binary datasets.     

On tests of symmetry,marginal homogeneity and quasi-symmetry in two-way contingency tables based on minimum φ-divergence estimator with constraints

The restricted minimum φ-divergence estimator, [Pardo, J.A., Pardo, L. and Zografos, K., 2002, Minimum φ-divergence estimators with constraints in multinomial populations. Journal of Statistical Planning and Inference, 104, 221–237], is employed to obtain estimates of the cell frequencies of an I×I contingency table under hypotheses of symmetry, marginal homogeneity or quasi-symmetry. The associated φ-divergence statistics are distributed asymptotically as chi-squared distributions under the null hypothesis. The new estimators and test statistics contain, as particular cases, the classical estimators and test statistics previously presented in the literature for the cited problems. A simulation study is presented, for the symmetry problem, to choose the best function φ₂ for estimation and the best function φ₁ for testing.     

Land-use and environmental pressures resulting from current and future bioenergy crop expansion: A review

Recent energy and climate policies, particularly in the developed world, have increased demand for bioenergy² as an alternative, which has led to both direct and indirect land-use changes and an array of environmental and socio-economic concerns. A comprehensive understanding of the land-use dynamics of bioenergy crop production is essential for the development of sustainable bioenergy and land-use policies. In this paper, we review the patterns and dynamics of land-use change associated with bioenergy crops (hereafter referred to as ’bioenergy-driven land-use change’). The review focuses on four regions as the most prominent locations in which these patterns and changes occur: Brazil; Indonesia and Malaysia; the United States of America (U.S.A.); and the European Union (EU). The review confirms that bioenergy-driven land-use change has affected and will impact most severely on the ’land- and resource-abundant’ developing regions, such as Brazil, where economic development takes priority over sustainable land-use policies, and the enforcement capability is limited. Opportunities for more effective policy are available through the development of international climate change policy (e.g. REDD under the UNFCCC), and certification criteria for sustainable bioenergy products (e.g. EU RED). However, bioenergy produced from no and/or less land-using feedstocks (e.g. wastes and residues), and their associated technologies must be given higher priority to minimise bioenergy-driven land-use change and its negative impacts.     

New cultural economies of marginality: revisiting the West Coast,South Island,New Zealand

Marginal regions have been the subject of political concern and remedial action in western states for several decades now. The West Coast of the South Island of New Zealand is an interesting case study in this regard, for recent economic growth has confounded earlier expectations of post-restructuring decline, while also contradicting several of the nostrums of new regionalism. In an effort to understand this trajectory, this paper draws on documents from public and private sector organisations, newspaper articles and field visits to examine developments in four key sectors of the West Coast's economy: mining, dairy farming, forestry and tourism. Economic growth is found to be closely linked to the cultivation of new markets for primary products, but efforts to rework the cultural dimensions of marginality have also been important. Value has been added to specific products through the insertion of references to the region's alpine and forested landscapes. Isolation and peripherality have been recast in more positive terms, echoing the broader reframing of New Zealand as a scenic, unspoiled destination. In adopting a cultural economic perspective on marginal regions, the paper illustrates the significance of symbolic forms of value, the potentially flexible nature of marginality as a discursive category, and the importance of the networks which connect regions to national and international flows of capital and tourists.     

Comparison of causal effect estimators under exposure misclassification

Over the past decades, various principles for causal effect estimation have been proposed, all differing in terms of how they adjust for measured confounders: either via traditional regression adjustment, by adjusting for the expected exposure given those confounders (e.g., the propensity score), or by inversely weighting each subject's data by the likelihood of the observed exposure, given those confounders. When the exposure is measured with error, this raises the question whether these different estimation strategies might be differently affected and whether one of them is to be preferred for that reason. In this article, we investigate this by comparing inverse probability of treatment weighted (IPTW) estimators and doubly robust estimators for the exposure effect in linear marginal structural mean models (MSM) with G-estimators, propensity score (PS) adjusted estimators and ordinary least squares (OLS) estimators for the exposure effect in linear regression models. We find analytically that these estimators are equally affected when exposure misclassification is independent of the confounders, but not otherwise. Simulation studies reveal similar results for time-varying exposures and when the model of interest includes a logistic link.     

Hypothesis Testing of Hazard Ratio Parameters in Marginal Models for Multivariate Failure Time Data

Marginal hazard models for multivariate failure time data have been studied extensively in recent literature. However, standard hypothesis test statistics based on the likelihood method are not exactly appropriate for this kind of model. In this paper, extensions of the three commonly used likelihood hypothesis test statistics are discussed. Generalized Wald, generalized score and generalized likelihood ratio tests for hazard ratio parameters in a marginal hazard model for multivariate failure time data are proposed and their asymptotic distributions examined. The finite sample properties of these statistics are studied through simulations. The proposed method is applied to data from Busselton Population Health Surveys.     

There's more to the picture than meets the eye: Measuring tangible and intangible capital in two marginal communities in rural Denmark

A missing link in economics has been what Veblen in 1908 termed intangible capital. This includes common norms, trust and high levels of cooperative performance. Intangibles are invisible to the eye and not easily measured in quantitative terms. They nevertheless involve visible, socioeconomic outcomes and should therefore rightly be seen as productive, like tangibles. Thus, uneven levels of intangible capital would explain Differential Economic Performance (DEP) between, say, two firms containing exactly the same stock of physical, economic and human capital. Despite this common sense observation, most economists have failed to see that ‘there's more to the picture than meets the eye’, as Neil Young once sang. We use statistical, historical and fieldwork data from two Danish, marginal rural communities both rich on intangible capital. This to show how intangible capital in the form of social, organisational and cultural capital is accumulated and utilised in situ, at the microlevel. We suggest that the difference between these two, very similar communities should be explained in their varying ability to utilise local stocks of tangible and intangible capital. Drawing on seminal ideas from Bourdieu [The forms of capital. In: Richardson, J.G. (Ed.) Handbook of Theory and Research for the Sociology of Education. Greenwood Press, New York, Westport, CT and London, 1986, pp. 241–58] and the DORA project [Bryden, Differential economic performance in rural areas. In: International Conference on Rural Communities and Identities in the Global Millennium. Malpasino University College, Nainamo, BC, Canada, 2000], we want to develop a ‘total capital’ assessment tool for mapping and measuring socioeconomic development in marginal rural communities. In this way, we hope to count in ‘all’ capital as Schultz [Investment in human capital. In: Kiker, B.F. (Ed.) Investment in Human Capital. Columbia, 1971, pp. 3–21] prophesised. This in order to explain what we term Differential Local Development (DLD), where ‘good’, sustainable development is associated with high economic performance and increase in population.