Using a standard decomposition of forecasts 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...

Using a Bayesian learning model with heterogeneity across agents, our study aims to identify the relative importance of alternative pathways through which professional forecasters disagree and reach consensus on the term structure of inflation and real GDP forecasts, resulting in different...

Using a standard decomposition of forecasts 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...

Using a Bayesian learning model with heterogeneity across agents, our study aims to identify the relative importance of alternative pathways through which professional forecasters disagree and reach consensus on the term structure of inflation and real GDP forecasts, resulting in different...

We estimate a Bayesian learning model with heterogeneity aimed at explaining the evolution of expert disagreement in forecasting real GDP growth and inflation over 24 monthly horizons for G7 countries during 1990-2007. Professional forecasters are found to begin and have relatively more success...

With the proliferation of quality multi-dimensional surveys, it becomes increasingly important for researchers to employ an econometric framework in which these data can be properly analyzed and put to their maximum use. In this chapter we have summarized such a framework developed in Davies and...

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...

We propose a new measure of differential interpretation in the context of a Bayesian learning model, which allows us to abstract from other sources of disagreement, such as differences in priors. We then develop a likelihood ratio statistic for testing the null hypothesis that agents interpret...

This paper provides a detailed analysis of the forecasts of real GDP, inflation and unemployment made by individual members of the Federal Open Market Committee (FOMC) for the period 1992–2003. Despite a general tendency for the committee members to underpredict real GDP over the sample...

This paper proposes three new panel unit root tests based on Zaykin et al. (2002)’s truncated product method. The first one assumes constant correlation between p-values and the latter two use sieve bootstrap that allows for general forms of cross-section dependence in the panel units. Monte...