Eurosystem …nally published its eagerly awaited in ‡ation forecast (as well as a forecast of GDP growth). It did so after having been provoked and prodded to do so, by the European Parlia-ment's Committee on Economic and Monetary A¤airs and a large number of external observers and commentators, since the announcement of the Eurosystem monetary-policy strategy in the fall of 1998. This Brie…ng P...

This paper presents theoretical models and their empirical results for the return and variance dynamics of German stocks. A factor structure is used in order to allow for a parsimonious modeling of the rst two moments of returns. Dynamic factor models with GARCH dynamics (GARCH(1,1)-M, IGARCH(1,1)-M, Nonlinear Asymmetric GARCH(1,1)-M and Glosten-Jagannathan-Runkle GARCH(1,1)-M) and three di ere...

Prediction Markets for Economic Forecasting Prediction markets – markets used to forecast future events – have been used to accurately forecast the outcome of political contests, sporting events, and, occasionally, economic outcomes. This chapter summarizes the latest research on prediction markets in order to further their utilization by economic forecasters. We show that prediction markets ha...

Budget forecasts are used by incumbent governments as a political tool to manage the elector particularly during an election cycle (Bruck and Stephan 2006). Incumbents apply budget forecasts strategically. They use short-term forecasts, which are more likely to be unbiased by political factors, annually. They rely on long-term forecasts, which are likely to be biased by political calculations, ...

Neural networks have been applied successfully to a broad range of fields such as finance, data mining, medicine, engineering, geology, physics and biology. In finance, neural networks have been used for stock market prediction, credit rating, bankruptcy prediction and economic indicator forecasts. In medicine, neural networks have been used extensively in medical diagnosis, detection and evalu...

Future highly renewable energy systems will couple to complex weather and climate dynamics. This coupling is generally not captured in detail by the open models developed in the power and energy system communities, where such open models exist. To enable modeling such a future energy system, we describe a dedicated large-scale dataset for a renewable electric power system. The dataset combines ...

Representation and modeling of economic uncertainty is addressed by different modeling methods, namely stochastic variables and probabilities, interval analysis, and fuzzy numbers, in particular triple estimates. Fo-cusing on discounted cash flow analysis numerical results are presented, comparisons are made between alter-native modeling methods, and characteristics of the methods are discussed.

Recently, “smart” urban infrastructure has been suggested as a strategy to mitigate social and economic risks posed by extreme weather events and to improve the sustainability of urban centers. Smart infrastructure is enabled by real-time sensing, modeling, optimization, and actuation, which permit the infrastructure to reason about the world around it and to autonomously reconfigure in order t...

This paper begins with a brief review of the recent experience using nonlinear models and ideas of chaos to model economic data and to provide forecasts that are better than linear models. The record of improvement is at best meager. The remainder of the paper examines some of the reasons for this lack of improvement. The concepts of “openness” and “isolation” are introduced, and a case is made...