http://lists.repec.org/mailman/listinfo/nep-for Forecasting 2012-02-08 Rob J Hyndman http://d.repec.org/n?u=RePEc:pra:mprapa:36147&r=for This paper investigates the accuracy of point and density forecasts of four DSGE models for inflation, output growth and the federal funds rate. Model parameters are estimated and forecasts are derived successively from historical U.S. data vintages synchronized with the Fed’s Greenbook projections. Point forecasts of some models are of similar accuracy as the forecasts of nonstructural large dataset methods. Despite their common underlying New Keynesian modeling philosophy, forecasts of different DSGE models turn out to be quite distinct. Weighted forecasts are more precise than forecasts from individual models. The accuracy of a simple average of DSGE model forecasts is comparable to Greenbook projections for medium term horizons. Comparing density forecasts of DSGE models with the actual distribution of observations shows that the models overestimate uncertainty around point forecasts. Wolters, Maik Hendrik 2012-01-23 DSGE models; forecasting; model uncertainty; forecast combination; density forecasts; real-time data; Greenbook http://d.repec.org/n?u=RePEc:gwc:wpaper:2012-001&r=for This article provides a discussion of Clements and Galvão’s “Forecasting with Vector Autoregressive Models of Data Vintages: US output growth and inflation.” Clements and Galvão argue that a multiple-vintage VAR model can be useful for forecasting data that are subject to revisions. Clements and Galvão draw a “distinction between forecasting future observations and revisions to past data,” which brings yet another real time data issue to the attention of forecasters. This comment discusses the importance of taking data revisions into consideration and compares the multiple-vintage VAR approach of Clements and Galvão to a state-space approach. Tara M. Sinclair 2012-01 Real time data, Evaluating forecasts, Forecasting practice, Time series, Econometric models http://d.repec.org/n?u=RePEc:kie:kieliw:1753&r=for Releases of the GDP are subject to revisions over time. This paper examines the predictability of German GDP revisions using forecast rationality tests. Previous studies of German GDP covering data until 1997 finds that revisions of real seasonally adjusted GDP are predictable. This paper uses a newly available real-time data to analyze the revisions of real seasonal adjusted GDP, of nominal unadjusted GDP, of the seasonal pattern, and of the GDP deflator for the period between 1992 and 2006. We find that the revisions of the nominal unadjusted GDP are unpredictable, but that the revisions of the price adjustments are predictable. Nevertheless, revisions of real seasonally adjusted GDP are hardly predictable and less well predictable compared to earlier studies. This lower predictability seems to be linked to the finding that revisions of seasonal adjustments are hardly predictable, too, and that their predictability decreased over time Jens Boysen-Hogrefe, Stefan Neuwirth 2012-01 Real-time data, GDP revisions, noise, news, forecasting, seasonal adjustment, price adjustment http://d.repec.org/n?u=RePEc:ags:saea12:119656&r=for This paper is designed to validate if the agricultural sector can once again be labeled as an instigator of the late-2000s Great Recession using the early warning models technique. The empirical results indicate that exposure to agribusiness operations does not necessarily enhance a banksâ tendency to fail. Li, Xiaofei, Escalante, Cesar L., Epperson, James E., Gunter, Lewell F. 2012 Agricultural Banking, Early warning signals, In-sample accuracy, Out-of-sample forecasting, Agricultural Finance, Research Methods/ Statistical Methods, G21, G32, G33, C01, http://d.repec.org/n?u=RePEc:ris:cieodp:2012_004&r=for Soil erosion is serious economic and environmental concern. Assessing soil erosion risk in the Alqueva dam watershed is urgently needed to conserve soil and water resources and prevent the accelerated dam siltation, taking into account the possible land-use changes, due to tourism development, intensification of irrigated farming and biomass production, as well as climate change. A comprehensive methodology that integrates Revised Universal Soil Loss Equation (RUSLE) model and Geographic Information Systems (GIS) with geostatistical techniques was adopted to study different land-use and management scenarios. The main objective of this study stage is to determine the soil erosion vulnerability of an agro-silvo pastoral system. The resultant soil erosion map shows an average of 14.1 t/ha/ year, with serious erosion risk (higher than 50 t/ha/year) in 4.3% of area. The highest values are associated mainly to high slopes and low vegetation. The final prediction maps for soil erosion and for each factor considered, can be used as a solid base to create a Decision Support System so as to provide specific procedures for decision-makers, promoting for sustainability of the ecosystems, reducing the risk of erosion and consequently increase lifetime of dam, under various land use and management scenarios Ferreira, Vera, Panagopoulos, Thomas 2012-01-23 Soil Erosion; Land-use; Geostatistic; RUSLE; Geographic Information System http://d.repec.org/n?u=RePEc:hhs:sdueko:2012_001&r=for Chartist and fundamentalist models have proven to be capable of replicating stylized facts on speculative markets. In general, this is achieved by specifying nonlinear interactions of otherwise linear asset price expectations of the respective trader groups. This paper investigates whether or not regressive and extrapolative expectations themselves exhibit significant nonlinear dynamics. The empirical results are based on a new data set from the European Central Bank Survey of Professional Forecasters on oil price expectations. In particular, we find that forecasters form destabilizing expectations in the neighborhood of the fundamental value, whereas expectations tend to be stabilizing in the presence of substantial oil price misalignment. Reitz, Stefan, Rülke, Jan-Christoph, Stadtmann, Georg 2012-01-03 Agent based models; nonlinear expectations; survey data