Background In 2003 the Wall Street Journal (WSJ) introduced its Monthly Economic Forecasting Survey. Each month the WSJ polls between 50 and 60 well-known economic experts asking their forecasts of future key economic variables such as GDP, inflation, US treasury rates, unemployment, housing starts, and other data. The forecasts are always for set times of the year, namely the ends of the first...

The application of traditional forecasting methods to discrete count data yields forecasts that are non-coherent. That is, such methods produce non-integer point and interval predictions which violate the restrictions on the sample space of the integer variable. This paper presents a methodology for producing coherent forecasts of low count time series. The forecasts are based on estimates of t...

This paper derives revenue-maximising and risk-constrained strategies for stochastic generators participating in electricity markets with a single-price balancing mechanism. The solution to this problem requires forecasts of multiple processes: the participant’s energy production, day-ahead and balancing electricity prices, and the system length. By formulating the problem from a probabilistic ...

Many variations such as the annual cycle of sea surface temperatures can be considered to be smooth functions and are appropriately described using methods from functional data analysis. This study de-nes a class of Functional Auto-Regressive (FAR) models which can be used as robust predictors for making forecasts of entire smooth functions in the future. The methods are illustrated and compare...

The application of traditional forecasting methods to discrete count data yields forecasts that are non-coherent. That is, such methods produce non-integer point and interval predictions which violate the restrictions on the sample space of the integer variable. This paper presents a Bayesian methodology for producing coherent forecasts of low count time series. The forecasts are based on estim...

Generating one-month-ahead systematic (beta) risk forecasts is common place in financial management. This paper evaluates the accuracy of these beta forecasts in three return measurement settings; monthly, daily and 30 minutes. It is found that the popular Fama-MacBeth beta from 5 years of monthly returns generates the most accurate beta forecast among estimators based on monthly returns. A rea...

We present a comprehensive modelling framework aimed at quantifying the response of agricultural commodity prices to changes in their potential determinants. The problem model uncertainty is assessed explicitly by concentrating on specification selection based quality short-term out-of-sample forecasts (1 12 months ahead) for price wheat, soybeans and corn. Univariate multivariate autoregressiv...

This paper evaluates the effectiveness of selected volatility models in forecasting Value-at-Risk (VaR) for 1-day and 10-day horizons. The latter is the actual reporting horizon required by the Basel Committee on Banking Supervision, but not considered in existing studies. The autoregressive stochastic volatility (Taylor, 1982) is found to be less effective than simpler ARCH type models such as...

This paper estimates a structural VAR model for key Swiss macroeconomics variables with quarterly data from 1974-1999 which allows the identification of a monetary shock with plausible impulse response patterns. Conditional forecasts generated by this model are used to analyse monetary policy in the in the new policy framework of SNB adopted in late 1999. In this exercise we attempt to take int...