Economics

This paper establishes stylized facts about the cyclicality of real consumer wages and real producer wages in Germany. As detrending methods we apply the deterministic trend model, the Beveridge-Nelson decomposition, the Hodrick-Prescott filter, the Baxter-King filter and the structural time series model. The detrended data are analyzed both in the time domain and in the frequency domain. The great advantage of an analysis in the frequency domain is that it allows to assess the relative importance of particular frequencies for the behavior of real wages. In the time domain we find that both real wages display a procyclical pattern and lag behind the business cycle. In the frequency domain the consumer real wage lags behind the business cycle and shows an anticyclical behavior for shorter time periods, whereas for longer time spans a procyclical behavior can be observed. However, for the producer real wage the results in the frequency domain remain inconclusive.

This article provides new insights into the cyclical behavior of consumer and producer real wages in the USA and Germany. We apply two methods for the estimation of the cyclical components from the data: the approach based on the structural time series models and the ARIMA–model–based approach combined with the canonical decomposition and a band–pass filter. We examine the extracted cycles drawing on two wavelet concepts:
wavelet coherence and wavelet phase angle. In contrast to the analysis in the time or frequency domains, wavelet analysis allows for the identification of possible changes in cyclical patterns over time. From the findings of our study, we can infer that the USA
and Germany differ with respect to the lead–lag relationship of real wages and the business cycle. In the USA, both real wages are leading the business cycle in the entire time interval. The German consumer real wage is, on the other hand, lagging the business cycle. For the German producer real wage, the lead–lag pattern changes over time. We also find that real wages in the USA as well in Germany are procyclical or acyclical until 1980 and countercyclical thereafter.

Spectral analysis is one of the most important areas of time series econometrics. The
use of spectral measures is widespread in dierent science elds such as economics,
physics, engineering, geology. The SPECTRAN toolbox has been developed to facilitate
the application of spectral concepts to univariate as well as to multivariate series. It
oers a variety of frequency-domain techniques and supports the statistical inference.
It also provides convenient tools for the examination of the results, e.g.functions for
writing the output to a le or functions specially designed for plotting the estimated
spectral measures. The key feature of SPECTRAN is the user-friendliness embodied
in, e.g., the central function spectran which performs the whole analysis with default
settings, but also gives the user the possibility to adjust them. This document sets
out the most relevant spectral concepts and their implementation in SPECTRAN.
Finally, three examples shall illustrate the application of dierent toolbox function to
macroeconomic data.

This article proposes a new multivariate method to construct business cycle indicators.
The method is based on a decomposition into trend{cycle and irregular. To derive the
cycle, a multivariate band{pass lter is applied to the estimated trend-cycle. The whole
procedure is fully model-based. Using a set of monthly and quarterly US time series, two
monthly business cycle indicators are obtained for the US. They are represented by the
smoothed cycles of real GDP and the industrial production index. Both indicators are
able to reproduce previous recessions very well. Series contributing to the construction
of both indicators are allowed to be leading, lagging or coincident relative to the business
cycle. Their behavior is assessed by means of the phase angle and the mean phase angle
after cycle estimation. The proposed multivariate method can serve as an attractive tool
for policy making, in particular due to its good forecasting performance and quite simple
setting. The model ensures reliable realtime forecasts even though it does not involve
elaborate mechanisms that account for, e.g., changes in volatility.

Unemployment is persistently high in many European countries. It is frequently attributed to 'functional deficiencies of labor markets', even though to a large extent caused by demand and supply shifts in goods markets and financial markets. The PhD program Globalization and Employment therefore focuses on the employment effects of structural change and macroeconomic developments and encourages research by granting scholarships. The dissertation projects address topics such as new technologies, processes of internationalization and system transformation, endogenous finance constraints and various strategies of fiscal and monetary policy.

Unemployment is persistently high in many European countries. It is frequently attributed to 'functional deficiencies of labor markets', even though to a large extent caused by demand and supply shifts in goods markets and financial markets. The PhD program Globalization and Employment therefore focuses on the employment effects of structural change and macroeconomic developments and encourages research by granting scholarships. The dissertation projects address topics such as new technologies, processes of internationalization and system transformation, endogenous finance constraints and various strategies of fiscal and monetary policy.

Die Situation der griechischen Wirtschaft spitzt sich aktuell dramatisch zu. Eine Besserung der Lage setzt einen wettbewerbsfähigen Exportsektor voraus. Dabei wäre die Wiedereinführung der Drachme eine Option, die der Autor allerdings für schwer durchführbar hält.

A multi-agent simulation embodying a theory of innovation networks has been built and used to suggest a number of policy-relevant conclusions. The simulation animates a model of innovation (the successful exploitation of new ideas) and this model is briefly described. Agents in the model representing firms, policy actors, research labs, etc. each have a knowledge base that they use to generate 'artefacts' that they hope will be innovations. The success of the artefacts is judged by an oracle that evaluates each artefact using a criterion that is not available to the agents. Agents are able to follow strategies to improve their artefacts either on their own (through incremental improvement or by radical changes), or by seeking partners to contribute additional knowledge. It is shown though experiments with the model's parameters that it is possible to reproduce qualitatively the characteristics of innovation networks in two sectors: personal and mobile communications and biotechnology.

An agent-based simulation model representing a theory of the dynamic processes involved in innovation in moder n knowledge-based industries is described. The agentbased approach allows the representation of heterogeneous agents that have individual and varying stocks of knowledge. The simulation is able to model uncertainty, historical change, effect of failure on the agent population, and agent learning from experience, from individual research and from partners and collaborators. The interactions between the agents occur ...

A comparison of the current structures and dynamics of UK and German biotechnology-based industries reveals a striking convergence of industrial organisations and innovation directions in both countries, as we will show in this paper. This counteracts propositions from theoretical frameworks such as the varieties-of-capitalism hypothesis and the national innovation systems approach which suggest substantial differences between the industrial structures of the countries due to differing institutional frameworks. In this paper, we ...

A multi-agent simulation embodying a theory of innovation networks has been built and used to suggest a number of policy-relevant conclusions. The simulation animates a model of innovation (the successful exploitation of new ideas) and this model is briefly described. Agents in the model representing firms, policy actors, research labs, etc. each have a knowledge base that they use to generate 'artefacts' that they hope will be innovations. The success of the artefacts is judged by an oracle that evaluates each ...

This paper explores the self-organising principles of horizontally-integrated innovation networks. It is shown that such networks can self-organising in environments where the co-ordination and production of new knowledge is itself a complex, dynamic and highly non-linear processes. The paper argues the development of a self-organisation perspective of innovation networks has two advantages. First, it provides a general framework of dynamic systems in which different strands of a highly fragmented literature can be drawn together. Second, formal self-organisation modelling techniques can provide interesting new insights into the micro-macro processes driving dynamic innovation systems.

Economics, this branch of economic literature which deals with dynamic processes causing qualitative transformation of economies basically driven by the introduction of novelties in their various and multifaceted forms. By its very nature, innovation and in particular technological innovation is the most exponent and most visible form of novelty. Therefore it is not very surprising that Neo-Schumpeterian Economics today has its most prolific fields in the studies of innovation and learning behavior on the micro-level of an economy, the studies on industry dynamics on the meso-level and studies of innovation driven growth and competitiveness on the macro-level of the economy. From a general point of view, however, the future developmental potential of socio-economic systems i.e. innovation in a very broad understanding encompassing besides technological innovation also organizational, institutional and social innovation has to be considered as the normative principle of Neo-

Modern knowledge-intensive economies are complex social systems where intertwining factors are responsible for the shaping of emerging industries: the self-organising interaction patterns and strategies of the individual actors (an agency-oriented pattern) and the institutional frameworks of different innovation systems (a structureoriented pattern). In this paper, we examine the relative primacy of the two patterns in the development of innovation networks, and find that both are important. In order to investigate the relative significance of strategic decision making by innovation network actors and the roles played by national institutional settings, we use an agent-based model of knowledge-intensive innovation networks, SKIN. We experiment with the simulation of different actor strategies and different access conditions to capital in order to study the resulting effects on innovation performance and size of the industry. Our analysis suggests that actors are able to compensate for structural limitations through strategic collaborations. The implications for public policy are outlined. K eywords: innovation networks, agent-based social simulation, innovation systems 2 3 4 5 6 7 8 ). In Europe´s knowledge-based economies the mechanisms of knowledge creation and utilisation have been changing, with an increasing emphasis on the formation of innovation networks, that is, networks which connect innovative firms, government agencies, research institutes and sources of venture capital. Economic sociology and new innovation economics consider the growing complexity of knowledge, the accelerating pace of knowledge creation, and the shortening of industry life cycles to be responsible for the rising importance of innovation networks (e.g. Powell et al. 2005, Eliasson 1995). Knowledge-intensive industries such as information and communication technologies (ICT) a undergone structural changes in the direction of these collective modes of knowledge production and application. Emerging industries, such as those based on new materials and nanotechnologies as well as knowledge-based services, are also developing along these lines. Combining knowledge resources in social networks enables innovation and learning that are difficult to provide by other means.