Estimation of parametric and nonparametric models for univariate claim severity distributions - an approach using R

This paper presents an analysis of motor vehicle insurance claims relating to vehicle damage and to associated medical expenses. We use univariate severity distributions estimated with parametric and non-parametric methods. The methods are implemented using the statistical package R. Parametric analysis is limited to estimation of normal and lognormal distributions for each of the two claim types. The nonparametric analysis presented involves kernel density estimation. We illustrate the benefits of applying transformations to data prior to employing kernel based methods. We use a log-transformation and an optimal transformation amongst a class of transformations that produces symmetry in the data. The central aim of this paper is to provide educators with material that can be used in the classroom to teach statistical estimation methods, goodness of fit analysis and importantly statistical computing in the context of insurance and risk management. To this end, we have included in the Appendix of this paper all the R code that has been used in the analysis so that readers, both students and educators, can fully explore the techniques described

Cross-country polarisation in CO2 emissions per capita in the European Union: changes and explanatory factors

In this study, we analyse the degree of polarisation-a concept fundamentally different from that of inequality-in the international distribution of CO2 emissions per capita in the European Union. It is analytically relevant to examine the degree of instability inherent to a distribution and, in the analysed case, the likelihood that the distribution and its evolution will increase or decrease the chances of reaching an agreement. Two approaches were used to measure polarisation: the endogenous approach, in which countries are grouped according to their similarity in terms of emissions, and the exogenous approach, in which countries are grouped geographically. Our findings indicate a clear decrease in polarisation since the mid-1990s, which can essentially be explained by the fact that the different groups of countries have converged (i.e. antagonism among the CO2 emitters has decreased) as the contribution of energy intensity to between-group differences has decreased. This lower degree of polarisation in CO2 distribution suggests a situation more conducive to the possibility of reaching EU-wide agreements on the mitigation of CO2 emissions.

Policy evaluation and uncertainty about the effects of oil prices on economic activity

This paper addresses the issue of policy evaluation in a context in which policymakers are uncertain about the effects of oil prices on economic performance. I consider models of the economy inspired by Solow (1980), Blanchard and Gali (2007), Kim and Loungani (1992) and Hamilton (1983, 2005), which incorporate different assumptions on the channels through which oil prices have an impact on economic activity. I first study the characteristics of the model space and I analyze the likelihood of the different specifications. I show that the existence of plausible alternative representations of the economy forces the policymaker to face the problem of model uncertainty. Then, I use the Bayesian approach proposed by Brock, Durlauf and West (2003, 2007) and the minimax approach developed by Hansen and Sargent (2008) to integrate this form of uncertainty into policy evaluation. I find that, in the environment under analysis, the standard Taylor rule is outperformed under a number of criteria by alternative simple rules in which policymakers introduce persistence in the policy instrument and respond to changes in the real price of oil.

Indirect likelihood inference

Given a sample from a fully specified parametric model, let Zn be a given finite-dimensional statistic - for example, an initial estimator or a set of sample moments. We propose to (re-)estimate the parameters of the model by maximizing the likelihood of Zn. We call this the maximum indirect likelihood (MIL) estimator. We also propose a computationally tractable Bayesian version of the estimator which we refer to as a Bayesian Indirect Likelihood (BIL) estimator. In most cases, the density of the statistic will be of unknown form, and we develop simulated versions of the MIL and BIL estimators. We show that the indirect likelihood estimators are consistent and asymptotically normally distributed, with the same asymptotic variance as that of the corresponding efficient two-step GMM estimator based on the same statistic. However, our likelihood-based estimators, by taking into account the full finite-sample distribution of the statistic, are higher order efficient relative to GMM-type estimators. Furthermore, in many cases they enjoy a bias reduction property similar to that of the indirect inference estimator. Monte Carlo results for a number of applications including dynamic and nonlinear panel data models, a structural auction model and two DSGE models show that the proposed estimators indeed have attractive finite sample properties.

Giacomo Becattini and the Marshall's method. A Schumpeterian approach

The studies of Giacomo Becattini concerning the notion of the "Marshallian industrial district" have led a revolution in the field of economic development around the world. The paper offers an interpretation of the methodology adopted by Becattini. The roots are clearly Marshallian. Becattini proposes a return to the economy as a complex social science that operates in historical time. We adopt a Schumpeterian approach to the method in economic analysis in order to highlight the similarities between the Marshall and Becattini's approach. Finally the paper uses the distinction between logical time, real time and historical time which enable us to study the "localized" economic process in a Becattinian way.

A parameter-free approach for solving combinatorial optimization problems through biased randomization of efficient heuristics

This paper discusses the use of probabilistic or randomized algorithms for solving combinatorial optimization problems. Our approach employs non-uniform probability distributions to add a biased random behavior to classical heuristics so a large set of alternative good solutions can be quickly obtained in a natural way and without complex conguration processes. This procedure is especially useful in problems where properties such as non-smoothness or non-convexity lead to a highly irregular solution space, for which the traditional optimization methods, both of exact and approximate nature, may fail to reach their full potential. The results obtained are promising enough to suggest that randomizing classical heuristics is a powerful method that can be successfully applied in a variety of cases.

Stochastic time-concentration activity models for cytotoxicity in 3D brain cell cultures.

BACKGROUND: In vitro aggregating brain cell cultures containing all types of brain cells have been shown to be useful for neurotoxicological investigations. The cultures are used for the detection of nervous system-specific effects of compounds by measuring multiple endpoints, including changes in enzyme activities. Concentration-dependent neurotoxicity is determined at several time points. METHODS: A Markov model was set up to describe the dynamics of brain cell populations exposed to potentially neurotoxic compounds. Brain cells were assumed to be either in a healthy or stressed state, with only stressed cells being susceptible to cell death. Cells may have switched between these states or died with concentration-dependent transition rates. Since cell numbers were not directly measurable, intracellular lactate dehydrogenase (LDH) activity was used as a surrogate. Assuming that changes in cell numbers are proportional to changes in intracellular LDH activity, stochastic enzyme activity models were derived. Maximum likelihood and least squares regression techniques were applied for estimation of the transition rates. Likelihood ratio tests were performed to test hypotheses about the transition rates. Simulation studies were used to investigate the performance of the transition rate estimators and to analyze the error rates of the likelihood ratio tests. The stochastic time-concentration activity model was applied to intracellular LDH activity measurements after 7 and 14 days of continuous exposure to propofol. The model describes transitions from healthy to stressed cells and from stressed cells to death. RESULTS: The model predicted that propofol would affect stressed cells more than healthy cells. Increasing propofol concentration from 10 to 100 μM reduced the mean waiting time for transition to the stressed state by 50%, from 14 to 7 days, whereas the mean duration to cellular death reduced more dramatically from 2.7 days to 6.5 hours. CONCLUSION: The proposed stochastic modeling approach can be used to discriminate between different biological hypotheses regarding the effect of a compound on the transition rates. The effects of different compounds on the transition rate estimates can be quantitatively compared. Data can be extrapolated at late measurement time points to investigate whether costs and time-consuming long-term experiments could possibly be eliminated.

Haar wavelets-based approach for quantifying credit portfolio losses

This paper proposes a new methodology to compute Value at Risk (VaR) for quantifying losses in credit portfolios. We approximate the cumulative distribution of the loss function by a finite combination of Haar wavelet basis functions and calculate the coefficients of the approximation by inverting its Laplace transform. The Wavelet Approximation (WA) method is specially suitable for non-smooth distributions, often arising in small or concentrated portfolios, when the hypothesis of the Basel II formulas are violated. To test the methodology we consider the Vasicek one-factor portfolio credit loss model as our model framework. WA is an accurate, robust and fast method, allowing to estimate VaR much more quickly than with a Monte Carlo (MC) method at the same level of accuracy and reliability.

Physical activity in 22 African countries: results from the World Health Organization STEPwise approach to chronic disease risk factor surveillance.

BACKGROUND: Baseline physical activity data are needed to effectively plan programs and policies to prevent noncommunicable diseases, but for many African countries these data are lacking. PURPOSE: To describe and compare levels and patterns of physical activity among adults across 22 African countries. METHODS: Data from 57,038 individuals from 22 countries (11 national and 11 subnational samples) that participated in the STEPwise approach to chronic disease risk factor surveillance (2003-2009) were analyzed in 2010. The validated Global Physical Activity Questionnaire (GPAQ) was used to assess days and duration of physical activity at work, for transport, and during leisure time in a typical week. RESULTS: Overall, 83.8% of men and 75.7% of women met WHO physical activity recommendations (at least 150 minutes of moderate activity per week or equivalent). Country prevalence ranged from 46.8% (Mali) to 96.0% (Mozambique). Physical activity, both at work and for transport, including walking, had large contributions to overall physical activity, while physical activity during leisure time was rare in the analyzed countries. CONCLUSIONS: Physical activity levels varied greatly across African countries and population subgroups. Leisure time activity was consistently low. These data will be useful to inform policymakers and to guide interventions to promote physical activity.

A logistic regression approach to estimating customer profit loss due to lapses in insurance

This article focuses on business risk management in the insurance industry. A methodology for estimating the profit loss caused by each customer in the portfolio due to policy cancellation is proposed. Using data from a European insurance company, customer behaviour over time is analyzed in order to estimate the probability of policy cancelation and the resulting potential profit loss due to cancellation. Customers may have up to two different lines of business contracts: motor insurance and other diverse insurance (such as, home contents, life or accident insurance). Implications for understanding customer cancellation behaviour as the core of business risk management are outlined.

Epidemiology and Anthropology: an integrated approach dealing with bio-socio-cultural aspects as strategy for the control of endemic diseases

The control of endemic diseases has not attained the desired level of effectiveness in spite of the use of modern efficient thecnologies. The classic interventionist approach for the control of schistosomiasis is centered on systemic control of the snail hosts combined to large scale medical treatment and is usually carried out without social preocupation due to the assisted communities. It is easy to understand the interest and the ethical compromise of public health research while producing studies in which the biological and social determinants as well as the cultural components should be considered and also encompass the historical dimensions and symbolic representations. In face of the recent political decision in favor of decentralizations of health administration to municipal level, we suggest, in the present paper, an integrated approach for the epidemiological diagnosis of an endemic situation at local level. Theoretical and methodological aspects from both, epidemiology and anthropology are discussed. Epidemiological methods can be used to detect the dependent variables (those related to the human infection) and the independent variables (demographic, economic, sanitary and social). Another methodological approach of anthropological /etnographic nature can be conducted in order to make an articulation of the knowledge on the various dimensions or determinant levels of the disease. Mutual comprehension, between researchers and the people under investigation, on the dynamic transmission process would be relevant for a joint construction, at local level, of programmed actions for the control of endemic diseases. This would extend reflections on the health/disease process as a whole.

Les thyroïdites: une approche pour le médecin praticien [Thyroiditis: an approach for the practitioner].

Thyroiditis represent commonly encountered thyroid disorders in clinical medicine. Autoimmune thyroiditis is frequent with various forms: Hashimoto's thyroiditis, subacute thyroiditis with lymphocytic and post-partum variants. In the absence of any changes in volume and function, Hashimoto's thyroiditis can be seen in a setting of a thyroid nodule, elevation of thyroid autoantibodies and by the pathologist in surgical or cytological materials. Presenting with fever, cervical pain and subtle signs of hyperthyroidism, subacute thyroiditis (De Quervain) represents often a challenge for an early recognition and an appropriate treatment. Acute thyroiditis and Riedel's thyroiditis are very uncommon, with cervical mass and pain.

Syndrome de Sjögren: enfin une nouvelle approche de traitement [Sjögren's syndrome: a new approach to treatment].

Södgren's syndrome treatment has essentially been based on symptomatic approach and has been of limited efficacy. Novel biological therapies targeting B cells, a key player in the pathophysiology of the syndrome, have recently been tested in controlled clinical trials and raise the hope of improving glandular and extraglandular manifestations of Söigren's syndrome.