Conservatism and consensus-seeking among economic forecasters

This paper uses the track records of a panel of US economic forecasters participating in a consensus forecasting service to test for conservatism and consensus-seeking behaviour. The tests are based on a particular method-of-moments estimator, designed to allow for the heteroscedasticity and serial correlation which is inevitably present in errors from repeated forecasts for fixed target dates. Most forecasters prove to be conservative. When revising forecasts they give too much weight to their own past forecasts. Surprisingly, forecasters are not consensus-seeking but ‘variety-seeking’. When revising forecasts, they give too little weight to the known forecasts of other forecasters.     

Predicting the signs of forecast errors

The signs of forecast errors can be predicted using the difference between individuals' forecasts and the average of earlier forecasts of the same variable. It is possible to improve forecasts without worsening any. It is difficult to reconcile this result with the rational expectations hypothesis because the average of earlier forecasts is in the information set of the forecasters. Copyright © 2009 John Wiley & Sons, Ltd.     

Strategic bias,herding behaviour and economic forecasts

Professional forecasters can have other objectives as well as minimizing expected squared forecast errors. This paper studies whether the people or companies which make forecasts behave strategically with the aim of maximizing aspects such as publicity, salary or their prestige, or more generally to minimize some loss function; or whether, on the contrary, they make forecasts which resemble consensus forecasts (herding behaviour). This study also analyses whether, as forecasters gain more reputation and experience, they make more radical forecasts, that is, they deviate further from the consensus. For this the Livingston Survey is used, a panel of experts who make forecasts on the future evolution of the United States economy. Copyright © 2003 John Wiley & Sons, Ltd.     

The accuracy of individual and group forecasts from business outlook surveys

This paper reports on the accuracy of quarterly multiperiod predictions of inflation, real growth, unemployment and percentage changes in nominal GNP and two of its more volatile components. The survey data are highly differentiated; they cover 79 professional forecasters (mostly economists, analysts and corporate executives). Combining corresponding predictions from different sources can result in significant gains; thus the group mean forecasts are on the average over time more accurate than most of the corresponding sets of individual forecasts. But there is also a moderate degree of consistency in the relative performance of a sufficient number of the survey members, as evidenced in positive rank correlations among ratios of the individual to group root mean square errors.     

Heterogeneous Forecast Adjustment

There is ample empirical evidence that expert‐adjusted model forecasts can be improved. One way to potential improvement concerns providing various forms of feedback to the sales forecasters. It is also often recognized that the experts (forecasters) might not constitute a homogeneous group. This paper provides a data‐based methodology to discern latent clusters of forecasters, and applies it to a fully new large database with data on expert‐adjusted forecasts, model forecasts and realizations. For the data at hand, two clusters can clearly be identified. Next, the consequences of having clusters are discussed. Copyright © 2016 John Wiley & Sons, Ltd.     

Overreaction in Survey Exchange Rate Forecasts

We use survey data on five bilateral exchange rates to provide empirical evidence of the fact that professional forecasters of foreign exchange rates behave irrationally, in the specific sense that they respond inaccurately to available information in the market when forming their predictions. In particular, we find systematic biases in the forecasts resulting in the overreaction of analysts to past information contained in the exchange rate dynamics: forecasters change their prediction more than it would be rational on the basis of past realized changes. In addition, forecasters are heterogeneous in their irrationality: low performers in previous periods show a more pronounced overreaction effect. This can be read as an indication of perpetration of past errors and continued inability to learn from the past. In the second part of the paper, we exploit the novel structure of our dataset, which consists of survey data extracted from the Bloomberg platform and readily available to anyone. This feature allows us to consider their own and others' past forecasts as part of the information set that analysts use in making their predictions. By using past forecasts as proxies for relevant macroeconomic variables, we find evidence that analysts fail to correctly process not only the information contained in the spot rate past dynamics but also the information in this broader set. We see this as confirmation of the existence of inefficiency and heterogeneity between low and high performers also when full information is available. Copyright © 2014 John Wiley & Sons, Ltd.     

Adjusting for information content when comparing forecast performance

Cross‐institutional forecast evaluations may be severely distorted by the fact that forecasts are made at different points in time and therefore with different amounts of information. This paper proposes a method to account for these differences when analyzing an unbalanced panel of forecasts. The method computes the timing effect and the forecaster's ability simultaneously. Monte Carlo simulation demonstrates that evaluations that do not adjust for the differences in information content may be misleading. In addition, the method is applied to a real‐world dataset of 10 Swedish forecasters for the period 1999–2015. The results show that the ranking of the forecasters is affected by the proposed adjustment.     

The uses and abuses of ‘consensus’ forecasts

Clemen's (1989) review of the forecast-combining literature amply illustrates both the interest in and the importance of this subject. This article stresses the tautological properties of various consensus measures that assure their success relative to most individual forecasts. It confirms the finding of earlier studies that for each specific macroeconomic variable roughly one-third of individual forecasters are more accurate than a consensus. However, each individual does relatively poorly for some variable while the consensus, in contrast, necessarily never fails relative to most individuals. These results, like most previous studies, describe consensus measures that are synthetic constructs derived from a pre-existing set of individual forecasts. Strictly speaking, this contemporaneous consensus is not available to individual forecasters when their forecasts are made. A prior consensus measure, which is in their information sets, was relatively much less accurate than the contemporaneous measure. Nevertheless, a small subset of individual forecasters were generally inferior to the known, prior consensus forecast.     

Social networks and private spaces in economic forecasting

The outputs of economic forecasting—predictions for national economic indicators such as GDP, unemployment rates and inflation—are all highly visible. The production of these forecasts is a much more private affair, however, typically being thought of as the work of individual forecasters or forecast teams using their economic model to produce a forecast that is then made public. This conception over-emphasises the individual and the technical whilst silencing the broader social context through which economic forecasters develop the expertise that is essential for the credibility of their predictions. In particular, economic forecasts are given meaning and fine-tuned through the social and institutional networks that give forecasters access to the expertise of a heterogeneous mix of academics, policy-makers and business people. Within these broader groups, individual forecasters often create private forecast ‘clubs’, where subscribers have privileged access to the expertise of the economist, but where the forecasters also have privileged access to their clients’ own expert knowledge. In examining these aspects of the forecasters’ work I show that the visible and audible activities of modelling and forecasting are made possible and plausible by virtue of the modeller’s invisible interaction with a wider network.     

Efficient use of information in the formation of subjective industry forecasts

The paper considers the use of information by a panel of expert industry forecasters, focusing on their information-processing biases. The panel forecasts construction output by sector up to three years ahead. It is found that the biases observed in laboratory experiments, particularly ‘anchoring’, are observable. The expectations are formed by adjusting the previous forecast to take new information into account. By analysing forecast errors it is concluded that the panel overweight recently released information and do not understand the dynamics of the industry. However, their forecasts, both short and long term, are better than an alternative econometric model, and combining the two sources of forecasts leads to a deterioration in forecast accuracy. The expert forecasts can be ‘de-biased’, and this leads to the conclusion that it is better to optimally process information sources than to combine (optimally) alternative forecasts.     

Combining exchange rate forecasts: What is the optimal consensus measure?

In this paper a high-quality disaggregate database is utilized to examine whether individual forecasters produce efficient exchange rate predictions and also if the properties of the forecasts change when they are combined. The paper links a number of themes in the exchange rate literature and examines various methods of forecast combination. It is demonstrated, inter alia, that some forecasters are better than others, but that most are not as good as a naive no-change prediction. Combining forecasts adds to the accuracy of the predictions, but the gains mainly reflect the removal of systematic and unstable bias.     

Testing homogeneity of Japanese CPI forecasters

This paper investigates whether some forecasters consistently outperform others using Japanese CPI forecast data of 42 forecasters over the past 18 quarters. It finds that the accuracy rankings of 0, 1, 2, and 5‐month forecasts are significantly different from those that might be expected when all forecasters had equal forecasting ability. Moreover, their rankings of the relative forecast levels are also significantly different from a random one. Copyright © 2009 John Wiley & Sons, Ltd.     

Do US Macroeconomic Forecasters Exaggerate their Differences?

Application of the Bernhardt et al. (Journal of Financial Economics 2006; 80 (3): 657–675) test of herding to the calendar‐year annual output growth and inflation forecasts suggests forecasters tend to exaggerate their differences, except at the shortest horizon, when they tend to herd. We consider whether these types of behaviour can help to explain the puzzle that professional forecasters sometimes make point predictions and histogram forecasts which are mutually inconsistent. Copyright © 2015 John Wiley & Sons, Ltd.     

How well do analysts forecast interest rates?

This paper examines interest rate forecasts made for the period 1982–90 and examines three issues: (1) Is there a general agreement among analysts about the level of interest rates six months in the future? (2) Are all the forecasters equally good? (3) Are the forecasts valuable to prospective users? We use distributions of the cross-sections of forecasts, Friedman's statistic for analysis of variance by rank, and tests of independence between forecasts and outcomes to examine these questions. We conclude that there usually was a consensus among analysts, that there was no significant difference in the ability to forecast short-term rates but there was a difference with respect to the long-term predictions, and that these forecasts were not significantly better than random walk forecasts.     

A comparative evaluation of objective and subjective weather forecasts in the united states

This paper is concerned primarily with the evaluation and comparison of objective and subjective weather forecasts. Operational forecasts of three weather elements are considered: (1) probability forecasts of precipitation occurrence, (2) categorical (i.e. non-probabilistic) forecasts of maximum and minimum temperatures and (3) categorical forecasts of cloud amount. The objective forecasts are prepared by numerical-statistical procedures, whereas the subjective forecasts are based on the judgements of individual forecasters. In formulating the latter, the forecasters consult information from a variety of sources, including the objective forecasts themselves. The precipitation probability forecasts are found to be both reliable and skilful, and evaluation of the temperature/cloud amount forecasts reveals that they are quite accurate/skilful. Comparison of the objective and subjective forecasts of precipitation occurrence indicates that the latter are generally more skilful than the former for shorter lead times (e.g. 12–24 hours), whereas the two types of forecasts are of approximately equal skill for longer lead times (e.g. 36–48 hours). Similar results are obtained for the maximum and minimum temperature forecasts. Objective cloud amount forecasts are more skilful than subjective cloud amount forecasts for all lead times. Examination of trends in performance over the last decade reveals that both types of forecasts for all three elements increased in skill (or accuracy) over the period, with improvements in objective forecasts equalling or exceeding improvements in subjective forecasts. The role and impact of the objective forecasts in the subjective weather forecasting process are discussed in some detail. The need to conduct controlled experiments and other studies of this process, with particular reference to the assimilation of information from different sources, is emphasized. Important characteristics of the forecasting system in meteorology are identified, and they are used to describe similarities and differences between weather forecasting and forecasting in other fields. Acquisition of some of these characteristics may be beneficial to other forecasting systems.     

Are forecasters reluctant to revise their predictions? Some German evidence

People are reluctant to admit mistakes. This could also be true of economic forecasters. If revisions of past forecasts are costly, then it will become optimal for forecasters to only partially adjust a past forecast in the light of new information. The unwillingness to admit to the mistake in the old forecast generates a bias of the new forecast in the direction of the old forecast. We test this hypothesis for the joint predictions of the Association of German Economic Research Institutes over the last 35 years. We find some evidence for such a bias and compute the implied unwillingness to revise forecasts. Copyright © 2006 John Wiley & Sons, Ltd.     

The role of judgement in macroeconomic forecasting

This paper examines the rationale for, and influence of, judgemental adjustments in macroeconomic forecasting, using two particular forecasts for the UK economy recently published by the National Institute of Economic and Social Research and the London Business School. It is found that in some cases such adjustments have a major effect on the forecasts and can also explain some of the differences in the two rival forecasts. However the number of adjustments for which this is true is not great. An implication of these findings is that, if these forecasts can be regarded as typical, then macroeconomic forecasters should be urged to give a reasonable account of the role of judgemental adjustments in their forecasts, particularly since the amount of information which would be required is not likely to be excessive.     

Strategic bias and professional affiliations of macroeconomic forecasters

This paper investigates strategic motives of macroeconomic forecasters and the effect of their professional affiliations. The ‘wishful expectations hypothesis’ suggests that a forecaster predicts what his employer wishes. The ‘publicity hypothesis’ argues that forecasters are evaluated by both accuracy and ability to generate publicity, and that forecasters in industries that emphasize publicity most will make most extreme and least accurate predictions. The ‘signaling hypothesis’ asserts that an extreme forecast signals confidence in own ability, because incompetent forecasters would mimic others to avoid public notice. Empirical evidence from a 26‐year panel of annual GDP forecasts is con‐sistent with the publicity hypothesis. This indicates that conventional tests of rationality are biased toward rejecting the rational expectations hypothesis. Copyright ? 2008 John Wiley & Sons, Ltd.     

Linear constraints,robust-weighting and efficient composite modeling

Recent studies have shown that composite forecasting produces superior forecasts when compared to individual forecasts. This paper extends the existing literature by employing linear constraints and robust regression techniques in composite model building. Security analysts forecasts may be improved when combined with time series forecasts for a diversified sample of 261 firms with a 1980-1982 post-sample estimation period. The mean square error of analyst forecasts may be reduced by combining analyst and univariate time series model forecasts in constrained and unconstrained ordinary least squares regression models. These reductions are very interesting when one finds that the univariate time series model forecasts do not substantially deviate from those produced by ARIMA (0,1,1) processes. Moreover, security analysts' forecast errors may be significantly reduced when constrained and unconstrained robust regression analyses are employed.     

Professional economic forecasts: Are they worth their costs?

Most economic forecast evaluations dating back 20 years show that professional forecasters add little to the forecasts generated by the simplest of models. Using various types of forecast error criteria, these evaluations usually conclude that the professional forecasts are little better than the no-change or ARIM A type forecast. It is our contention that this conclusion is mistaken because the conventional error criteria may not capture why forecasts are ma& or how they are used. Using forecast directional accuracy, the criterion which has been found to be highly correlated with profits in an interest rate setting, we find that professional GNP forecasts dominate the cheaper alternatives. Moreover, there appears to be no systematic relationship between this preferred criterion and the error measures used in previous studies.