Are some forecasters' probability assessments of macro variables better than those of others?

We apply the bootstrap test of DíAgostino et al. (2012) to determine whether some forecasters are able to make superior probability assessments to others. In contrast to the findings of DíAgostino et al. (2012) for point predictions, there is more evidence that some individuals really are better than others. The testing procedure controls for the different economic conditions the forecasters may face, given that each individual responds to only a subset of the surveys. One possible explanation for the different findings for point predictions and histograms is explored: that newcomers may make less accurate histogram forecasts than experienced respondents given the greater complexity of the task

Subjective and ex post forecast uncertainty : US inflation and output growth

Survey respondents who make point predictions and histogram forecasts of macrovariables reveal both how uncertain they believe the future to be, ex ante, as well as their expost performance. Macroeconomic forecasters tend to be overconÖdent at horizons of a year or more, but over-estimate the uncertainty surrounding their predictions at short horizons

Why are survey forecasts superior to model forecasts?

We investigate two characteristics of survey forecasts that are shown to contribute to their superiority over purely model-based forecasts. These are that the consensus forecasts incorporate the effects of perceived changes in the long-run outlook, as well as embodying departures from the path toward the long-run expectation. Both characteristics on average tend to enhance forecast accuracy. At the level of the individual forecasts, there is scant evidence that the second characteristic enhances forecast accuracy, and the average accuracy of the individualforecasts can be improved by applying a mechanical correction. Keywords: consensus forecast, model-based forecasts, long-run expectations.consensus forecast ; model-based forecasts ; long-run expectations JEL Classification: C53 ; E37

Internal consistency of survey respondents.forecasts : Evidence based on the Survey of Professional Forecasters

We ask whether the different types of forecasts made by individual survey respondents are mutually consistent, using the SPF survey data. We compare the point forecasts and central tendencies of probability distributions matched by individual respondent, and compare the forecast probabilities of declines in output with the probabilities implied by the probability distributions. When the expected associations between these different types of forecasts do not hold for some idividuals, we consider whether the discrepancies we observe are consistent with rational behaviour by agents with asymmetric loss functions.Rationality ; probability forecasts ; probability distributions

Rounding of probability forecasts : The SPF forecast probabilities of negative output growth

We consider the possibility that respondents to the Survey of Professional Forecasters round their probability forecasts of the event that real output will decline in the future. We make various assumptions about how forecasters round their forecasts, including that individuals have constant patterns of responses across forecasts. Our primary interests are the impact of rounding on assessments of the internal consistency of the probability forecasts of a decline in real output and the histograms for annual real output growth, and on the relationship between the probability forecasts and the point forecasts of quarterly output growth.Rounding ; probability forecasts ; probability distributions

Explanations of the inconsistencies in survey respondents'forecasts

A comparison of the point forecasts and the central tendencies of probability distributions of inflation and output growth of the SPF indicates that the point forecasts are sometimes optimistic relative to the probability distributions. We consider and evaluate a number of possible explanations for this finding, including the degree of uncertainty concerning the future, computational costs, delayed updating, and asymmetric loss. We also consider the relative accuracy of the two sets of forecasts.Rationality ; point forecasts ; probability distributions

Do Professional Forecasters Pay Attention to Data Releases?

We present a novel approach to assessing the attentiveness of professional forecasters to news about the macroeconomy. We find evidence that professional forecasters, taken as a group, do not always update their estimates of the current state of the economy to re‡ect the latest releases of revised estimates of key data. Key words: Professional forecasters ; data revisions; inattention JEL classification: C53

Probability Distributions or Point Predictions? Survey Forecasts of US Output Growth and Inflation

We consider whether survey respondents’probability distributions, reported as histograms, provide reliable and coherent point predictions, when viewed through the lens of a Bayesian learning model, and whether they are well calibrated more generally. We argue that a role remains for eliciting directly-reported point predictions in surveys of professional forecasters. Key words: probability distribution forecasts ; point forecasts ; Bayesian learning JEL classification: C53

Economic forecasting in a changing world

This article explains the basis for a theory of economic forecasting developed over the past decade by the authors. The research has resulted in numerous articles in academic journals, two monographs, Forecasting Economic Time Series, 1998, Cambridge University Press, and Forecasting Nonstationary Economic Time Series, 1999, MIT Press, and three edited volumes, Understanding Economic Forecasts, 2001, MIT Press, A Companion to Economic Forecasting, 2002, Blackwells, and the Oxford Bulletin of Economics and Statistics, 2005. The aim here is to provide an accessible, non-technical, account of the main ideas. The interested reader is referred to the monographs for derivations, simulation evidence, and further empirical illustrations, which in turn reference the original articles and related material, and provide bibliographic perspective

Pooling of Forecasts

We consider forecasting using a combination, when no model coincides with a non-constant data generation process (DGP). Practical experience suggests that combining forecasts adds value, and can even dominate the best individual device. We show why this can occur when forecasting models are differentially mis-specified, and is likely to occur when the DGP is subject to deterministic shifts. Moreover, averaging may then dominate over estimated weights in the combination. Finally, it cannot be proved that only non-encompassed devices should be retained in the combination. Empirical and Monte Carlo illustrations confirm the analysis.