Using a standard decomposition of forecast errors into common and idiosyncratic shocks, we show that aggregate forecast uncertainty can be expressed as the disagreement among the forecasters plus the perceived variability of future aggregate shocks. Thus the reliability of disagreement as a...

Recursive-weight forecast combination is often found to an ineffective method of improving point forecast accuracy in the presence of uncertain instabilities. We examine the effectiveness of this strategy for forecast densities using (many) vector autoregressive (VAR) and autoregressive (AR)...

We propose new methods for comparing the out-of-sample forecasting performance of two competing models in the presence of possible instabilities. The main idea is to develop a measure of the relative local forecasting performance for the two models, and to investigate its stability over time by...

We propose two new procedures for comparing the mean squared prediction error (MSPE) of a benchmark model to the MSPEs of a small set of alternative models that nest the benchmark. Our procedures compare the benchmark to all the alternative models simultaneously rather than sequentially, and do...

This paper considers the 'real-time' forecast performance of the Federal Reserve staff, time-series models, and an estimated dynamic stochastic general equilibrium (DSGE) model-the Federal Reserve Board's Estimated, Dynamic, Optimization-based (Edo) model. We evaluate forecast performance using...

This paper demonstrates the potential problem in using existing economics journal rankings to evaluate the research productivity of scholars by constructing a new ranking of economics journals and articles. Based on 2142 econometrics sample articles published from 2000 to 2005, our ranking...

We provide an empirical framework for assessing the distributional properties of daily speculative returns within the context of the continuous-time jump diffusion models traditionally used in asset pricing finance. Our approach builds directly on recently developed realized variation measures...

This paper studies in some detail a class of high-frequency-based volatility (HEAVY) models. These models are direct models of daily asset return volatility based on realised measures constructed from high-frequency data. Our analysis identifies that the models have momentum and mean reversion...

This paper is a study of the application of Bayesian exponentially tilted empirical likelihood to inference about quantile regressions. In the case of simple quantiles we show the exact form for the likelihood implied by this method and compare it with the Bayesian bootstrap and with Jeffreys'...

The information contained in a large panel dataset is used to date historical turning points and to forecast future ones. We estimate groups of series with similar time series dynamics and link the groups with a dynamic structure. The dynamic structure identifies a group of leading and a group...