Catálogo de publicaciones - libros

We estimate the sensitivity of the regional growth forecast in the year 2002 due to expected changes in the travel time (TT) matrix. We use a dynamic panel model with spatial effects where the spatial dimension enters the explanatory variables in different ways. The spatial dimension is based on geographical distance between 227 cells in central Europe and the travel time matrix based on average train travel times. The regressor variables are constructed by a) the average past growth rates, where the travel times are used as weights, b) the average travel times across all cells (made comparable by index construction), c) the gravity potential variables based on GDP per capita, employment, productivity and population and d) dummy variables and other socio-demographic variables. We find that for the majority of the cells the relative differences in growth for the year 2020 is rather small. But there are differences as how many regions will benefit from improved train networks: GDP, employment, and population forecasts respond differently.

One serious problem in deep-hole drilling is the formation of a dynamic disturbance called spiralling which causes holes with several lobes. Since such lobes are a severe impairment of the bore hole the formation of spiralling has to be prevented. Gessesse et al. (1994) explain spiralling by the coincidence of bending modes and multiples of the rotary frequency. This they derive from an elaborate finite elements model of the process.

In online measurements we detected slowly changing frequency patterns similar to those calculated by Gessesse et al. We therefore propose a method to estimate the parameters determining the change of frequencies over time from spectrogram data. This allows to significantly simplify the usage of the explanation of spiralling in practice because the finite elements model has to be correctly modified for each machine and tool assembly while the statistical method uses observable measurements. Estimating the variation of the frequencies as good as possible opens up the opportunity to prevent spiralling by e.g. changing the rotary frequency.

In 2004 six major socio-economic regions according to The Nomenclature of Territorial Units for Statistics (NUTS) were established. The so called NUTS1 regions were formed as groups of voivodships (NUTS2 regions). Some of the authorities expressed their concerns that joining better regions with less developed in one might raise GDP per capita statistic in region, thus reducing the expected flows of structural funds. In the paper we try to verify these concerns. We study the level of economic development of all districts in Poland, and its spatial diversification, using the methods of synthetic taxonomic indexes of development and clustering methods.

The academic discussion about technical analysis has a long tradition, in American literature as well as in the German scientific community. Lo et al. (2000) laid the foundation for empirical research on the “classical” technical indicators (like “head-and-shoulders” formations) with their paper “Foundations of Technical analysis”.

The candlestick technique is based on the visual recognition of patterns called “Candlesticks”, a special method of visualizing the behavior of asset prices. Candlesticks are very popular in Asia and their popularity is growing in western countries. Until now there has not been done much empirical research concerning the performance of technical analysis with candlesticks. This is probably due to the fact that no automatic and deterministic way to classify candlestick patterns has been developed thus far.

The purpose of this work is to lay the basis for future empirical investigations and to develop a systematic approach by which candlestick charts can be classified.

The loss distribution of a credit portfolio is considered within the framework of a Bernoulli-mixture model where in each rating grade the stochastic Bernoulli-parameter follows an autoregressive stationary process. Changes in the loss distribution are discussed when the unconditional view is replaced by a conditional view where information from the last period is taken into account. This relates to the lively debate among practitioners whether regulatory capital should incorporate point-in-time or through-the-cycle aspects. Calculations are carried out in a model estimated with real data from a large retail portfolio.

Kernel methods for classification problems map data points into feature spaces where linear separation is performed. Detecting linear relations has been the focus of much research in statistics and machine learning, resulting in efficient algorithms that are well understood, with many applications including credit scoring problems. However, the choice of more appropriate kernel functions using nonlinear feature mapping may still improve this classification performance. We show, how different kernel functions contribute to the solution of a credit scoring problem and we also show how to select and compare such kernels.

In this article we present downside risk measures such as: Value-at-Risk - and Conditional Value-at-Risk - . We established these measures based on the principal components analysis. The principal components analysis is usually applied to complex systems that depend on a large number of factors where one wishes to identify the smallest number of new variables that explain as much of the variability in the system as possible. The first few principal components usually explain the most of historical variability. In our research we used the prices of electric energy from the Day Ahead Market (DAM) of the Polish Power Exchange from 30.03.03 to 27.03.04. We conclude by discussing practical applications of the results of our research in risk management on the Polish Power Exchange.

The following research is guided by the hypothesis that products chosen on a shopping trip in a supermarket can indicate the preference interdependencies between different products or brands. The bundle chosen on the trip can be regarded as the result of a global utility function. More specifically: the existence of such a function implies a cross-category dependence of brand choice behavior. It is hypothesized that the global utility function related to a product bundle results from the marketing-mix of the underlying brands. Several approaches exist to describe the choice of specific categories from a set of many alternatives. The models are discussed in brief; the multivariate logit approach is used to estimate a model with a German data set.

Marketing problems often involve binary classification of customers into “buyers” versus “non-buyers” or “prefers brand A” versus “prefers brand B”. These cases require binary classification models such as logistic regression, linear, and quadratic discriminant analysis. A promising recent technique for the binary classification problem is the Support Vector Machine (Vapnik (1995)), which has achieved outstanding results in areas ranging from Bioinformatics to Finance. In this paper, we compare the performance of the Support Vector Machine against standard binary classification techniques on a marketing data set and elaborate on the interpretation of the obtained results.

Over the last two decades the analytical toolbox for examining the properties needed to claim causal relationships has been significantly extended. New approaches to the theory of causality rely on the concept of ‘intervention’ instead of ‘association’. Under an axiomatic framework they elaborate the conditions for safe causal inference from nonexperimental data. (Spirtes et. al., 2000; Pearl, 2000) teaches us that the same independence relationships (or covariance matrix) may have been generated by numerous other graphs representing the cause-effect hypotheses. ICT combines elements of graph theory, statistics, logic, and computer science. It is not limited to parametric models in need of quantitative (ratio or interval scaled) data, but also operates much more generally on the observed conditional independence relationships among a set of qualitative (categorical) observations. Causal inference does not appear to be restricted to experimental data. This is particularly promising for research domains such as consumer behavior where policy makers and managers are unwilling to engage in experiments on real markets. A case example highlights the potential use of Inferred Causation methodology for analyzing the marketing researchers’ belief systems about their scientific orientation.