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...

Over the past two decades the United States has experienced substantial increases in the number of bank failures, however, surprisingly few banks have failed during the 2001 recession. This paper explores the relationship between economic cycles and bank health. We find that economic forecasts provide little additional information over bank-specific financial data during prosperous times, possi...

Weather forecasting is a complex process that involves numerous specialized fields of expertise. The output from computationally intensive numerical weather prediction (NWP) models forms the starting point of the forecasting process. Expert forecasters have both a general knowledge of large-scale weather systems and specific knowledge about the idiosyncratic behavior of local scale weather phen...

While a host of economic variables have been identified in the literature with the apparent in-sample ability to predict the equity premium, Goyal and Welch (2007) find that these variables fail to deliver consistent out-of-sample forecast gains relative to the historical average. Imposing theoretically motivated restrictions on individual predictive regression models, Campbell and Thompson (20...

This paper presents a fine-scale (30 meter resolution) regional land cover modeling system, based on the SLEUTH cellular automata model, that was developed for a 257000 km area comprising the Chesapeake Bay drainage basin in the eastern United States. As part of this effort, we developed a new version of the SLEUTH model (SLEUTH-3r), which introduces new functionality and fit metrics that subst...

While economic variables have been used extensively to forecast the U.S. bond risk premia, little attention has been paid to the use of technical indicators which are widely employed by practitioners. In this paper, we fill this gap by studying the predictive ability of using a variety of technical indicators vis-á-vis the economic variables. We find that the technical indicators have significa...

Highly resolved precipitation forecasts are necessary in many applications, especially in mountain meteorology and flash flood forecasts for smallto medium-sized alpine watersheds. Here we present precipitation forecasts simulated by the limited area model ALADIN applying different grid resolutions (1x = 10 km and 4 km). Target area of the investigations is the Alpine Ticino-Verzasca-Maggia wat...

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.

While a host of economic variables have been identified in the literature with the apparent in-sample ability to predict the equity premium, Goyal and Welch (2007) find that these variables fail to deliver consistent out-of-sample forecast gains relative to the historical average. In this paper, we demonstrate that despite the failure of individual predictive regression model forecasts to outpe...

Fan chart or Monte Carlo simulations for assessing the uncertainty of inflation forecasts in Romania? Mihaela Simionescu To cite this article: Mihaela Simionescu (2014) Fan chart or Monte Carlo simulations for assessing the uncertainty of inflation forecasts in Romania?, Economic Research-Ekonomska Istraživanja, 27:1, 629-644, DOI: 10.1080/1331677X.2014.970453 To link to this article: http://dx...