Much research shows that the ratings that critics, judges, and consumers assign to wines are heteroscedastic. A rating observed is one draw from a latent distribution that is wine- and judge-specific. Estimating the shape of a rating’s distribution by minimizing a sum of cross entropies has been proposed and tested. This article proposes a method of improving the accuracy of that estimate by using information about the context of a wine competition or cross-section ratings data. Tests using the distributions implied by 90 blind triplicate ratings show that the sum of squared errors for the solution using context or cross-section information is 50% more accurate than not using such information and over 99% more accurate than ignoring the uncertainty about a rating.

We explore the impact of the 2013 Common Agricultural Policy (CAP) reform on spatial price co-movements between the main European Union (EU) wine producer markets (i.e., Spain, France, and Italy). We consider monthly prices from January 2005 to January 2020 and R-Vine copula models, splitting the time period considered in December 2012 to track the changes in (1) the degree of integration, (2) the central markets, and (3) the potential asymmetries after the 2013 CAP reform. The results indicate an increasing overall price dependence from one sub-period to another, with Spain succeeding Italy as the central EU market. We also show asymmetry between Italy and France in the upper tail before 2013 and between Spain and France in the lower tail after 2013.

Analyses and aggregations of the ratings that wine critics and judges assign to wines are made difficult by stochastic error and biases that remain even when wines are assessed blind to price, label, capsule, and closure. Stochastic error is due to the partially random nature of ratings. Cognitive and omitted-variable biases are due to anchoring, expectation, serial position, commercial, and other factors. Differences in decanting, filtering, aeration, and temperature can also affect ratings.

Much research shows that the ratings that judges assign to the same wine are uncertain. And while the ratings may be independent, research also shows that they are not identically distributed. Thus, an acute difficulty in ratings-related research and in calculating consensus among judges is that each rating is one observation drawn from a latent distribution that is wine- and judge-specific. What can be deduced about the shape of a latent distribution from one observation? A simple maximum entropy estimator is proposed to describe the distribution of a rating observed. The estimator can express the implications of zero, one, a few blind replicates, and many observations. Several tests of the estimator show that results are consistent with the results of experiments with blind replicates and that results are more accurate than results based on observed ratings alone.

Using telematics technology, insurers are able to capture a wide range of data to better decode driver behavior, such as distance traveled and how drivers brake, accelerate, or make turns. Such additional information also helps insurers improve risk assessments for usage-based insurance, a recent industry innovation. In this article, we explore the integration of telematics information into a classification model to determine driver heterogeneity. For motor insurance during a policy year, we typically observe a large proportion of drivers with zero accidents, a lower proportion with exactly one accident, and a far lower proportion with two or more accidents. We here introduce a cost-sensitive multi-class adaptive boosting (AdaBoost) algorithm we call SAMME.C2 to handle such class imbalances. We calibrate the algorithm using empirical data collected from a telematics program in Canada and demonstrate an improved assessment of driving behavior using telematics compared with traditional risk variables. Using suitable performance metrics, we show that our algorithm outperforms other learning models designed to handle class imbalances.

Using remotely sensed land-cover data in 1994 and 2014, and cross-sectional survey data in 2014, this study examines the association between land use and cover change and agricultural productivity in northern Ghana. We document a significant expansion of crop land and settlements (productive use) at the expense of natural vegetation cover. Land areas converted from natural cover to productive use have higher maize yield (0.17 tons per hectare) and harvest value (1,021 Ghanaian Cedi) compared with those converted from bare soil to productive cover. Moreover, areas that were covered by shrubs or savannah in 1994 were more productive in 2014 relative to bare soils in 1994. Although our data do not allow us to establish causality, the evidence suggests the importance of past land-cover conditions in affecting current agricultural performance, especially in resource-stricken settings where conservation and restoration practices are not as common.

This paper shows how the well-known Bornhuetter–Ferguson claims-reserving method can be extended by applying fuzzy methods. The a priori information for the ultimate claims derives from market statistics, organisational data, etc. and might contain vagueness. Likewise, the parameters of the claims development pattern can be vague or are adapted, retrospectively, due to subjective judgement. With the help of fuzzy numbers we develop new predictors for the ultimate claims. Furthermore, we quantify the uncertainty of the ultimate claims for single and aggregated accident years.

The class of spectrally positive Lévy processes is a frequent choice for modelling loss processes in areas such as insurance or operational risk. Dependence between such processes (e.g. between different lines of business) can be modelled with Lévy copulas. This approach is a parsimonious, efficient and flexible method which provides many of the advantages akin to distributional copulas for random variables. Literature on Lévy copulas seems to have primarily focussed on bivariate processes. When multivariate settings are considered, these usually exhibit an exchangeable dependence structure (whereby all subset of the processes have an identical marginal Lévy copula). In reality, losses are not always associated in an identical way, and models allowing for non-exchangeable dependence patterns are needed. In this paper, we present an approach which enables the development of such models. Inspired by ideas and techniques from the distributional copula literature we investigate the procedure of nesting Archimedean Lévy copulas. We provide a detailed analysis of this construction, and derive conditions under which valid multivariate (nested) Lévy copulas are obtained. Our results are discussed and illustrated, notably with an example of model fitting to data.

LL601 is a genetically modified rice variety and unapproved for commercial use. Its presence was found in commercial shipments of U.S. rice in 2006. This article explores its impact on prices and volume marketed for both the United States and Thailand, the major export competitor. The results show a significantly adverse but short duration effect on the U.S. rice market and little to no effect on the Thai rice market.

Using cross-sector and cross-country data, this paper evidences that rent seeking influenced the allocation of CO2 emission permits in the two first phases of the European emissions trading scheme. Industry lobbies effectively used the 'job loss' and 'competitiveness' arguments, as unemployment proxy variables significantly impacted the allocation in both phases, and carbon intensity influenced it in the second phase. The countries that adopted a partial auction scheme also gave relatively more permits and in particular to the politically more powerful sectors. This suggests a compensation mechanism and supports the assumption of a political tradeoff between the quantity of permits issued and the decision between free grant and auction. It also confirms that the initial allocation is not neutral in the presence of special interest lobbying.