The use of log-transformed data has become standard in macroeconomic forecasting with VAR models. However, its appropriateness in the context of out-of-sample forecasts has not yet been exposed to a thorough empirical investigation. With the aim of filling this void, a broad sample of VAR models is employed in a multi-country setup and approximately 16 Mio. pseudo-out-of-sample forecasts are ev...

We incorporate external information extracted from the European Central Bank’s Survey of Professional Forecasters into predictions a Bayesian VAR using entropic tilting and soft conditioning. The resulting conditional forecasts significantly improve plain BVAR point density forecasts. Importantly, we do not restrict at specific quarterly horizon but their possible paths over several horizons jo...

Due to the rapid increase in deployment and high penetration of solar power generation worldwide, solar power generation forecasting has become critical to variable generation integration planning, and within utility and independent system operator (ISO) operations. Utilities and ISOs require day ahead and hour ahead as well as intra-hour solar power forecasts for core operations solar power pr...

This paper examines a strategy for structuring one type of domain knowledge for use in extrapolation. It does so by representing information about causality and using this domain knowledge to select and combine forecasts. We use five categories to express causal impacts upon trends: growth, decay, supporting, opposing, and regressing. An identification of causal forces aided in the determinatio...

Several authors, including Andersen and Bollerslev (1998), stress the importance of long-term volatility dependence for value-at-risk (VaR) prediction. The present paper addresses multiple-period market risk forecasts under long memory persistence in market volatility. To this aim, we propose volatility forecasts based on a combination of the GARCH(1,1)-model with potentially fat-tailed and ske...

Bayesian dynamic models, stochastic simulation and Bayesian econometrics. of Rio de Janeiro in 1993 and is presently a lecturer of Statistics at Federal University of Parann a (Brazil). Research interests include Bayesian inference, stochastic simulatio n and Bayesian dynamic models. Abstract Forecasting the levels of vector autoregressive (VAR) log-transformed time series has shown to be awkwa...

As solar thermal and photovoltaic generation begin to have a larger role in electrical generation in California, the California Independent System Operators needs to accommodate their variable nature in its forecasting and dispatching. This project reviews and evaluates current knowledge and models for forecasting solar resources and considers options for improving forecasts through RD&D and ad...

This paper develops an estimated hybrid model that combines the micro-founded DSGE model with the flexibility of the theoretical VAR model. The model is estimated via the maximum likelihood technique based on quarterly data on real Gross National Product (GNP), consumption, investment and hours worked, for the South African economy, over the period of 1970:1 to 2000:4. Based on a recursive esti...

In a recent pilot study, errors in forecasts of future returns showed little differences when arithmetic and geometric mean calculations served as the basis for the projections. Using annual returns on the DJIA and S&P for 1954 to 2007, 1-year-, 5-year-ahead, and 10-year-ahead forecasts were made based on 5-, 10-, 15-, and 20-year histories based on both arithmetic and geometric averages. This ...

We decompose financial return series via wavelets into different time scales to analyse their information content regarding the volatility of the returns. Moreover, we investigate the information of each scale and discuss the decomposition of daily Value-at-Risk (VaR) forecasts. By an extensive empirical analysis, we analyse financial assets in calm and turmoil market times and show that daily ...