The New Keynesian Model and the Long-Run Vertical Phillips Curve: Does It Hold for Germany?

New-Keynesian macroeconomic models typically assume that any long-run trade-off between inflation and unemployment is ruled out. While this appears to be a reasonable characterization of the US economy, it is less clear that the natural rate hypothesis necessarily holds in a European country like Germany where hysteretic effects may invalidate it. Inspired by the framework developed by Farmer (2000) and Beyer and Farmer (2002), we investigate the long-run relationships between the interest rate, unemployment and inflation in West Germany from the early 1960s up to 2004 using a multivariate co-integration analysis technique. The results point to a structural break in the late 1970s. In the later time period we find for West German data a strong negative correlation between the trend components of inflation and unemployment. We show that this finding contradicts the natural rate hypothesis, introduce a version of the New Keynesian model which allows for some hysteresis and compare the effectiveness of monetary policy in these two models. In general, a policy rule with an aggressive response to a rise in unemployment performs better in a model with hysteretic characteristics than in a model without.Cointegration; Vector error correction model; Unemployment; Phillips curve; Hysteresis

Measuring the Effects of Monetary Policy in the Euro Area: The Role of Anticipated Policy

This paper investigates within a SVAR framework the effects of anticipated monetary policy in the euro area. Building on a procedure recently proposed by Cochrane yielding the response of output to an anticipated monetary policy impulse, we show that in the past twenty years anticipated monetary policy had a considerable influence on output. Moreover, we compute the output effects of the systematic monetary policy response to aggregate demand and supply shocks. We find that monetary policy pursues a counter-cyclical policy in response to demand shocks and is pro-cyclical with regard to supply shocks, even though there are considerable lags.Vector Autoregression, Systematic Monetary Policy, Historical Decomposition

Euroland: New conditions for economic policy

The cyclical situation at the beginning of the European Monetary Union (EMU) is favorable: The upswing in Euroland has firmed, unemployment is going down, and inflation is low. However, economic growth outside the new currency area has weakened significantly during 1998, and fears are mounting that the crises in various regions of the world economy could endanger the current expansion in Euroland. Against this background, the significance of external conditions for the business cycle in Euroland — as well as the regional structure of exports — is analyzed. An important issue for an adequate design of economic policy is to what extent capacities in Euroland are currently utilized and whether cyclical unemployment is still significant. In addition, it is important to know whether the business cycles in the individual countries converge or not. In light of the findings from these analyses, the course of monetary, fiscal, and wage policy is evaluated in order to assess the outlook for Euroland until the end of 1999. --

Methodological issues in testing the marginal productivity theory

Previous tests of the marginal productivity theory have been criticized on several grounds reviewed by the authors. One important deficiency has been the small number of factor inputs entered in the production functions. In 1978 Gottschalk suggested a method to estimate production functions with many inputs by assuming that the production process can be split into subprocesses. This reduces the probability of multicollinearity. The authors show that the method depends on an additional assumption. Tinbergen has developed a method for avoiding this assumption. Its application to American cross-section (state) data did not alter the estimated coefficients greatly

Survey on Unsupervised Domain Adaptation for Semantic Segmentation for Visual Perception in Automated Driving

Deep neural networks (DNNs) have proven their capabilities in the past years and play a significant role in environment perception for the challenging application of automated driving. They are employed for tasks such as detection, semantic segmentation, and sensor fusion. Despite tremendous research efforts, several issues still need to be addressed that limit the applicability of DNNs in automated driving. The bad generalization of DNNs to unseen domains is a major problem on the way to a safe, large-scale application, because manual annotation of new domains is costly, particularly for semantic segmentation. For this reason, methods are required to adapt DNNs to new domains without labeling effort. This task is termed unsupervised domain adaptation (UDA). While several different domain shifts challenge DNNs, the shift between synthetic and real data is of particular importance for automated driving, as it allows the use of simulation environments for DNN training. We present an overview of the current state of the art in this research field. We categorize and explain the different approaches for UDA. The number of considered publications is larger than any other survey on this topic. We also go far beyond the description of the UDA state-of-the-art, as we present a quantitative comparison of approaches and point out the latest trends in this field. We conduct a critical analysis of the state-of-the-art and highlight promising future research directions. With this survey, we aim to facilitate UDA research further and encourage scientists to exploit novel research directions

GivEn -- Shape Optimization for Gas Turbines in Volatile Energy Networks

This paper describes the project GivEn that develops a novel multicriteria optimization process for gas turbine blades and vanes using modern "adjoint" shape optimization algorithms. Given the many start and shut-down processes of gas power plants in volatile energy grids, besides optimizing gas turbine geometries for efficiency, the durability understood as minimization of the probability of failure is a design objective of increasing importance. We also describe the underlying coupling structure of the multiphysical simulations and use modern, gradient based multicriteria optimization procedures to enhance the exploration of Pareto-optimal solutions