Dedication: In Honor of Elvin R. Latty

The smart grid concept is the grid design philosophy that diversifies the powergrids and the electricity markets. However a deep penetration of \prosumers" and distributedgeneration in urban environments could lead to significant problems from an electromagneticcompatibility (EMC) viewpoint. Traditional classification methods, used for small isolated systems, are inadequate tools to investigate, improve and evaluate mitigation measures for largedistributed infrastructures such as a smart grid. Therefore, an alternative classification method,originally developed to investigate the vulnerability of large distributed systems from intentionalelectromagnetic interference (IEMI), is used here. The method is used to analyse the smart gridconcept to investigate if the smart grid is, from an EMC and IEMI viewpoint for a large distributed system, an improvement or deterioration compared to traditional power grids (and theaspects that is attached to them)QC 20131010</p

How Can We Use the Result from a DEA Analysis? Identification of Firm-relevant Reference Units.

Two types of guidelines can be obtained from a DEA (data envelopment analysis) analysis. Firstly, the firm can reduce input or increase production according to the DEA results. Secondly, an inefficient firm might be able to identify reference units. This makes it possible for the inefficient firm to, on site, study production that is more efficient, and thereby get information on e.g. efficient organisational solutions. In this study, we focus on how to detect these firm-relevant reference units. While applying the existing methods for identification of reference units, i.e. the intensity variable method and the dominance method, on a data set concerning booking centres in the Swedish taxi market, shortcomings in these methods were identified. This motivates the development of a new method. This new method, the sphere measure, enables an inefficient unit to identify existing and efficient units that have the largest similarity with itself. The identified units will thus be firm-relevant reference units.reference units; firm-relevant; DEA

News text generation with adversarial deep learning

In this work we carry out a thorough analysis of applying a specific field within machine learning called generative adversarial networks, to the art of natural language generation; more specifically we generate news text articles in an automated fashion. To do this, we experimented with a few different architectures and representations of text, evaluated the results and used the information retrieved from the results, to create a model that should give the best result. For evaluation, we used perplexity and human evaluation. We also looked at the token distribution to see which model captures the texts most successfully. We show that it is possible to use generative adversarial networks to generate sequences of tokens that resemble natural language, but this does not yet reach the quality of human-written text. Further hyperparameter tuning and using a narrower-subjected corpus could improve the output

Forensic evaluation: A strategy for and results of an impact evaluation of a universal labor market program - The Swedish activity guarantee

To evaluate the effects of a universal program, i.e. all targeted get access, is associated with the problem that there are no individuals who represent the hypothetical or counterfactual state of being eligible to the program but not participating in it. In this study of a Swedish universal labor market program the Activity Guarantee we show how regional differences in the implementation of instructions on assignment to the program were utilized to create a control group representing the counterfactual state. After having gained access to a treatment and a control group, the authors evaluated the effects of the program on the probability of leaving unemployment and on the duration of unemployment. The effects estimated were statistically significant and indicate a clear positive effect of program participation. -- In dieser Analyse wird das Evaluierungsproblem untersucht, das entsteht, wenn die Effekte eines Programms untersucht werden sollen, das einschränkungslos für alle Mitglieder einer Zielgruppe gilt. Dann nämlich gibt es nicht mehr die Fälle, die den gegenteiligen Zustand repräsentieren, d.h. Anspruchsberechtigte, die nicht an dem Programm teilnehmen. In dieser Studie über ein in diesem Sinne universelles Arbeitsmarktprogramm in Schweden mit dem Titel Aktivitäts- Garantie wird gezeigt, wie regionale Unterschiede bei der Umsetzung der Vorschriften zur Zuweisung in das Programm genutzt werden, um eine Kontrollgruppe zu bilden. Nachdem der Zugang zu den Daten einer Teilnehmergruppe und einer Kontrollgruppe geklärt war, evaluierten die Autoren die Effekte des Programms auf die Abgangswahrscheinlichkeit aus und auf die Dauer der Arbeitslosigkeit. Die geschätzten Effekte waren statistisch signifikant und belegen einen eindeutig positiven Effekt einer Teilnahme an dem Programm.

A Poisson Ridge Regression Estimator

The standard statistical method for analyzing count data is the Poisson regression model, which is usually estimated using maximum likelihood (ML). The ML method is very sensitive to multicollinearity. Therefore, we present a new Poisson ridge regression estimator (PRR) as a remedy to the problem of instability of the traditional ML method. To investigate the performance of the PRR and the traditional ML approaches for estimating the parameters of the Poisson regression model, we calculate the mean squared error (MSE) using Monte Carlo simulations. The result from the simulation study shows that the PRR method outperforms the traditional ML estimator in all of the different situations evaluated in this paper.Poisson regression; maximum likelihood; ridge regression; MSE; Monte Carlo simulations; Multicollinearity

Performance of Some Ridge Parameters for Probit Regression: with Application on Swedish Job Search Data

In ridge regression the estimation of the ridge parameter is an important issue. This paper generalizes some methods for estimating the ridge parameter for probit ridge regression (PRR) model based on the work of Kibria et al. (2011). The performance of these new estimators are judged by calculating the mean square error (MSE) using Monte Carlo simulations. In the design of the experiment we chose to vary the sample size and the number of regressors. Furthermore, we generate explanatory variables that are linear combinations of other regressors, which is a common situation in economics. In an empirical application regarding Swedish job search data we also illustrate the benefits of the new method.probit regression; maximum likelihood; multicollinearity; ridge regression; MSE; job search

A New Ridge Regression Causality Test in the Presence of Multicollinearity

This paper analyzes and compares the properties of the most commonly applied versions of the Granger causality (GC) test to a new ridge regression GC test (RRGC), in the presence of multicollinearity. The investigation has been carried out using Monte Carlo simulations. A large number of models have been investigated where the number of observations, strength of collinearity, and data generating processes have been varied. For each model we have performed 10000 replications and studied seven different versions of the test. The main conclusion from our study is that the traditional OLS version of the GC test over-rejects the true null hypothesis when there are relatively high (but empirically common levels of) multicollinearity, while it is established that the new RRGC test will remedy or substantially decrease this problem.Granger causality test; multicollinearity; ridge parameters; size and power