Strompreismodelle sind seit der Liberalisierung der Elektrizitätsmärkte ein elementares Werkzeug bei der Bewertung von Risiken aus dem Großhandelsmarkt. Die Bereinigung der Daten von Saisonalitäten und Sprüngen so-wie die Kalibrierung der Modellparameter sind entscheidende Schritte für die Qualität der Bewertung. Dieser Artikel stellt die Problematik dar und zeigt die wesentlichen Effekte in einer empirischen Studie an EEX Marktpreisen.
This event study investigates the impact of the Japanese nuclear catastrophe in Fukushima-Daiichi on the daily utility stock prices in a five-week period following March 11, 2011. On a country level, French, German, Japanese, and U.S. firms are considered. On a segment level, the study distinguishes between nuclear utility and alternative energy firms.
The results from prior studies of past nuclear disasters that document a significant negative performance for utility stock prices can generally be confirmed for the nuclear energy sub-sample in France, Germany, and Japan. However, while the nuclear accidents in Three Miles Island (1979) and Chernobyl (1985) induced significant reactions in U.S. utility stock prices, only weak evidence for similar reactions to the Fukushima-Daiichi accident can be found in the United States.
Furthermore, there is also clear evidence that alternative energy stocks benefit from a nuclear accident. There are strong reactions in France, Germany, and Japan suggesting a possible forthcoming policy change in these countries. Finally, the results of the study indicate that markets were capable of pricing the newly available information quickly, supporting the concept of semi-strong market efficiency.
In a multivariate analysis that investigates determinants of SR investing, this study finds little influence of the demographic factors of gender and investment volume and none of educational
level. Furthermore, it shows that the regions investors allocate their money to are significant along with the preference toward the order of return, risk and liquidity. Moreover, there appears to be a gap between supply and demand of SR investments. Additionally, there are indications that a very important inducement for SR investing is the expectation of a high financial performance.
In the framework of Mixed Models, it is often of interest to provide an es- timate of the uncertainty in predictions for the random effects, customarily defined by the Mean Squared Error of Prediction (MSEP). To address this computation in the Generalized Linear Mixed Model (GLMM) context, a non-parametric Bootstrap algorithm is proposed. First, a newly developed Bootstrap scheme relying on random weighting of cluster contributions to the joint likelhood function of the model and the Laplace Approximation is used to create bootstrap replicates of the parameters. Second, these replicates yield in turn bootstrap samples for the random effects and for the responses. Third, generating predictions of the random effects employing the bootstrap samples of observations produces bootstrap replicates of the random effects that, in conjunction with their respective bootstrap samples, are used in the estimation of the MSEP. To assess the validity of the proposed method, two simulation studies are presented. The first one in the framework of Gaussian LMM, contrasts the quality of the proposed approach with respect to: (i) an- alytical estimators of MSEP based on second-order correct approximations, (ii) Conditional Variances obtained with a Bayesian representation and (iii) other bootstrap schemes, on the grounds of relative bias, relative efficiency and the coverage ratios of resulting prediction intervals. The second simu- lation study serves the purpose of illustrating the properties of our proposal in a Non-Gaussian GLMM setting, namely a Mixed Logit Model, where the alternatives are scarce.
This paper discusses the contribution of Cerioli et al. (Stat Methods Appl, 2018), where robust monitoring based on high breakdown point estimators is proposed for multivariate data. The results follow years of development in robust diagnostic techniques. We discuss the issues of extending data monitoring to other models with complex structure, e.g. factor analysis, mixed linear models for which S- and MM-estimators exist or deviating data cells. We emphasise the importance of robust testing that is often overlooked despite robust tests being readily available once S- and MM-estimators have been defined. We mention open questions like out-of-sample inference or big data issues that would benefit from monitoring.
The calibration of (low-cost) inertial sensors has become increasingly important over the past years since their use has grown exponentially in many applications going from unmanned aerial vehicle navigation to 3D-animation. However, this calibration procedure is often quite problematic since the signals issued from these sensors have a complex spectral structure and the methods available to estimate the parameters of these models are either unstable, computationally intensive and/or statistically inconsistent. This paper presents a new software platform for inertial sensor calibration based on the Generalized Method of Wavelet Moments which provides a computationally efficient, flexible, user-friendly and statistically sound tool to estimate and select from a wide range of complex models. The software is developed within the open-source statistical software R and is based on C++ language allowing it to achieve high computational performance.
Gene selection has become a common task in most gene expression studies. The objective of such research is often to identify the smallest possible set of genes that can still achieve good predictive performance. To do so, many of the recently proposed classification methods require some form of dimension-reduction of the problem which finally provide a single model as an output and, in most cases, rely on the likelihood function in order to achieve variable selection. We propose a new prediction-based objective function that can be tailored to the requirements of practitioners and can be used to assess and interpret a given problem. Based on cross-validation techniques and the idea of importance sampling, our proposal scans low-dimensional models under the assumption of sparsity and, for each of them, estimates their objective function to assess their predictive power in order to select. Two applications on cancer data sets and a simulation study show that the proposal compares favorably with competing alternatives such as, for example, Elastic Net and Support Vector Machine. Indeed, the proposed method not only selects smaller models for better, or at least comparable, classification errors but also provides a set of selected models instead of a single one, allowing to construct a network of possible models for a target prediction accuracy level.
Along the ever increasing data size and model complexity, an important challenge frequently encountered in constructing new estimators or in implementing a classical one such as the maximum likelihood estimator, is the computational aspect of the estimation procedure. To carry out estimation, approximate methods such as pseudo-likelihood functions or approximated estimating equations are increasingly used in practice as these methods are typically easier to implement numerically although they can lead to inconsistent and/or biased estimators. In this context, we extend and provide refinements on the known bias correction properties of two simulation based methods, respectively indirect inference and bootstrap, each with two alternatives. These results allow one to build a framework defining simulation based estimators that can be implemented for complex models. Indeed, based on a biased or even inconsistent estimator, several simulation based methods can be used to define new estimators that are both consistent and with reduced finite sample bias. This framework includes the classical method of indirect inference for bias correction without requiring specification of an auxiliary model. We demonstrate the equivalence between one version of the indirect inference and the iterative bootstrap, both correct sample biases up to the order n^3. The iterative method can be thought of as a computationally efficient algorithm to solve the optimization problem of the indirect inference. Our results provide different tools to correct the asymptotic as well as finite sample biases of estimators and give insight on which method should be applied for the problem at hand. The usefulness of the proposed approach is illustrated with the estimation of robust income distributions and generalized linear latent variable models.
Dysfunctional cognitions regarding weight and shape and their implications for self-esteem are considered core features of anorexia nervosa and bulimia nervosa. However, they have also been associated with the severity of binge eating disorder (BED). Therefore, they should be screened with appropriate instruments to tailor treatment to individual patient needs. The Mizes Anorectic Cognitions-Revised (MAC-R) is a self-report questionnaire that lists dysfunctional cognitions related to three hypothesized core beliefs typical of the psychopathology of eating disorders:weight and eating as the basis of approval from others; the belief that rigid self-control is fundamental to self-worth; and the rigidity of weight- and eating-regulation efforts.
