Estimating the strength of expert judgement: The case of US mortality forecasts

The use of expert judgement is an important part of demographic forecasting. However, because judgement enters into the forecasting process in an informal way, it has been very difficult to assess its role relative to the analysis of past data. The use of targets in demographic forecasts permits us to embed the subjective forecasting process into a simple time-series regression model, in which expert judgement is incorporated via mixed estimation. The strength of expert judgement is denned, and estimated using the official forecasts of cause-specific mortality in the United States. We show that the weight given to judgement varies in an improbable manner by age. Overall, the weight given to judgement appears too high. An alternative approach to combining expert judgement and past data is suggested.     

Judgmental adjustment of earnings forecasts

Earnings forecasts have received a great deal of attention, much of which has centered on the comparative accuracy of judgmental and objective forecasting methods. Recently, studies have focused on the use of combinations of subjective and objective forecasts to improve forecast accuracy. This research offers an extension on this theme by subjectively modifying an objective forecast. Specifically, ARIMA forecasts are judgmentally adjusted by analysts using a structured approach based on Saaty's (1980) analytic hierarchy process. The results show that the accuracy of the unadjusted objective forecasts can be improved when judgmentally adjusted.     

A model of calibration for subjective probability distributions and its application to aggregation of judgements

A simple model is proposed of the statistical structure underlying the calibration of auditors' subjective probability distributions for account balances, and potentially for other unknown quantities. The model relates calibration curve shape to two parameters which represent over- or underconfidence and over- or underestimation. It is fitted to data from expert auditors. Different types of account appear to have different calibration characteristics. The model helps predict approximately the effects on calibration of aggregating individual subject distributions. Aggregation improves accuracy, but produces a strong tendency towards underconfidence. One aggregation method, predicting the best judgement in the group and using it as the group judgement, is found to be quite effective, much better than averaging the fractiles of individual distributions.     

Effects of difficulty on judgemental probability forecasting of control response efficacy

A judgemental control task was framed as a problem of medical decision making. The control parameter of a recursive system (i.e. a patient) was initially set so that output (i.e. a diagnostic index) fell outside a designated criterion range (corresponding to health). Subjects were told to bring the system's output into the designated range by resetting this control parameter (by specifying the dose of a drug). After each of these control responses, they made a probabilistic forecast that it would have the desired effect. It was found that these forecasts were more overconfident when the control task was more difficult but that the reason for this varied. When difficulty was manipulated across subjects, there was little evidence that lower control performance was associated with any lowering of the probabilistic forecasts. When difficulty was manipulated within subjects, they did lower their forecasts for more difficult task variants but did so insufficiently. In fact, relations between probabilistic forecasts of control response efficacy and proportion of those responses that were actually effective was linear with a slope of 0.44.     

Subjective versus objective combining of forecasts: An experiment

The paper reports results of an experiment conducted to evaluate subjective versus objective combination of forecasts. The subjects were undergraduate students at Texas A&M. The students forecasted two different types of time series. The results found show that the subjective combination of forecasts improves their accuracy as compared with individual efforts. Four ex-ante weighting methods were also used to combine the forecasts. They all improve the accuracy of the forecasts. The best results, though, were from the subjective combination of forecasts.     

Forecasting contemporaneous aggregates and the combination of forecasts: The case of the U.K. monetary aggregates

A number of papers in recent years have investigated the problems of forecasting contemporaneously aggregated time series and of combining alternative forecasts of a time series. This paper considers the integration of both approaches within the example of assessing the forecasting performance of models for two of the U.K. monetary aggregates, £M3 and MO. It is found that forecasts from a time series model for aggregate £M3 are superior to aggregated forecasts from individual models fitted to either the components or counterparts of £M3 and that an even better forecast is obtained by forming a linear combination of the three alternatives. For MO, however, aggregated forecasts from its components prove superior to either the forecast from the aggregate itself or from a linear combination of the two.     

The role of forecasting in corporate strategy: The xerox experience

This paper presents the system of analysis used by the Xerox Corporation to relate the external environment to company decisions. The system is sophisticated and elaborate, comes to grips with such issues as product forecasting, market monitoring, activity monitoring, materials and labour cost analysis, and product price analysis. In addition, the system examines the longer-term issues associated with corporate strategy, with the more recent initiatives directed toward the strategic focus The Xerox case illustrates very well how externally provided forecasts of economic environments, both at home and abroad, can be used as inputs to a variety of econometric products to serve the individual corporation. The challenge in this work is to build the bridges from the external forces to the critical company decisions. That is a task which requires sophisticated tools and skilled professionals to accomplish. This case study shows what can be done.     

Collinearity and forecasting

Old saw: Collinearity will not harm forecasts as long as it continues into the forecast period. If, however, collinearity is unlikely to continue but has already harmed estimation, then corrective action (introduction of prior information) to improve estimates should improve forecasts. This paper marshals the diagnostic information needed to assess collinearity's continuance and, when required, to direct meaningful corrective action. The process is illustrated by an example involving forecasts using energy prices.     

Medium‐term forecasts of potential GDP and inflation using age structure information

Economic behaviour as well as economic resources of individuals vary with age. Swedish time series show that the age structure contains information correlated to medium‐term trends in growth and inflation. GDP gaps estimated by age structure regressions are closely related to conventional measures. Monetary policy is believed to affect inflation with a lag of 1 or 2 years. Projections of the population's age structure are comparatively reliable several years ahead and provide additional information to improve on 3–5 years‐ahead forecasts of potential GDP and inflation. Thus there is a potential scope for using age structure based forecasts as an aid to monetary policy formation. Copyright © 2004 John Wiley & Sons, Ltd.     

Revisions in analysts' earnings forecasts: evidence of non‐linear adaptive expectations

Prior studies use a linear adaptive expectations model to describe how analysts revise their forecasts of future earnings in response to current forecast errors. However, research shows that extreme forecast errors are less likely than small forecast errors to persist in future years. If analysts recognize this property, their marginal forecast revisions should decrease with the forecast error's magnitude. Therefore, a linear model is likely to be unsatisfactory at describing analysts' forecast revisions. We find that a non‐linear model better describes the relation between analysts' forecast revisions and their forecast errors, and provides a richer theoretical framework for explaining analysts' forecasting behaviour. Our results are consistent with analysts' recognizing the permanent and temporary nature of forecast errors of differing magnitudes. Copyright © 2000 John Wiley & Sons, Ltd.