Evaluation of forecast optimality in economics and finance has almost exclusively been conducted on the assumption of mean squared error loss under which forecasts should be unbiased and forecast errors serially uncorrelated at the single period horizon with increasing variance as the forecast...

In this paper, we explore the potential gains from alternative combinations of the surveyed forecasts in the ECB Survey of Professional Forecasters. Our analysis encompasses a variety of methods including statistical combinations based on principal components analysis and trimmed means,...

We develop novel forecasting methods for panel data with heterogeneous parameters and examine them together with existing approaches. We conduct a systematic comparison of their predictive accuracy in settings with different cross-sectional (N) and time (T) dimensions and varying degrees of...

Evaluation of forecast optimality in economics and finance has almost exclusively been conducted under the assumption of mean squared error loss. Under this loss function optimal forecasts should be unbiased and forecast errors should be serially uncorrelated at the single period horizon with...

Evaluation of forecast optimality in economics and finance has almost exclusively been conducted on the assumption of mean squared error loss under which forecasts should be unbiased and forecast errors serially uncorrelated at the single period horizon with increasing variance as the forecast...

Recent empirical work documents substantial disagreement in inflation expectations obtained from survey data. Furthermore, the extent of such disagreement varies systematically over time in a way that reflects the level and variance of current inflation. This paper offers a simple explanation...

We develop novel forecasting methods for panel data with heterogeneous parameters and examine them together with existing approaches. We conduct a systematic comparison of their predictive accuracy in settings with different cross-sectional (N) and time (T) dimensions and varying degrees of...

We propose a new approach to predictive density modeling that allows for MI- DAS e¤ects in both the ?rst and second moments of the outcome and develop Gibbs sampling methods for Bayesian estimation in the presence of stochastic volatility dy- namics. When applied to quarterly U.S. GDP growth...

We analyze the complete subset regression (CSR) approach of Elliott et al. (2013) in situations with many possible predictor variables. The CSR approach has the computational advantage that it can be applied even when the number of predictors exceeds the sample size. Theoretical results...

Recent empirical work documents substantial disagreement in inflation expectations obtained from survey data. Furthermore, the extent of such disagreement varies systematically over time in a way that reflects the level and variance of current inflation. This paper offers a simple explanation...