Sources of error in forecasts and expectations: U.K. economic models, 1984–8

This paper presents a comparative analysis of the sources of error in forecasts for the UK economy published over a recent four-year period by four independent groups. This analysis rests on the archiving at the ESRC Macroeconomic Modelling Bureau of the original forecasts together with all their accompanying assumptions and adjustments. A method of decomposing observed forecast errors so as to distinguish the contributions of forecaster and model is set out; the impact of future expectations treated in a ‘model-consistent’ or ‘rational’ manner is specifically considered. The results show that the forecaster's adjustments make a substantial contribution to forecast performance, a good part of which comes from adjustments that bring the model on track at the start of the forecast period. The published ex-ante forecasts are usually superior to pure model-based ex-post forecasts, whose performance indicates some misspecification of the underlying models.     

Forecasts and expectations: Some evidence for the U.K.

Forecasts from econometric models have been given a lot of publicity in the U.K. This paper examines the performance of five major models in forecasting inflation and the rate of growth. Two types of forecast are considered: the annual pre-budget ones and a quarterly series. It is suggested that public forecasts provide a cheap method of implementing economically rational expectations.     

Unbiasedness,efficiency and the combination of economic forecasts

This paper considers the problem of determining whether forecasts are unbiased and examines the implications this has for combining different forecasts. The practical issues of how economic forecasts might be combined are discussed. There is an empirical illustration of the procedures in which the properties of UK forecasts from the London Business School, the National Institute, the Henley Centre for Forecasting, Phillips and Drew and the OECD are examined.     

An evaluation of quarterly national institute forecasts

This paper examines the quarterly forecasts by the U.K. National Institute of Economic and Social Research of the rate of inflation and the change in real gross domestic product and its components for horizons of one to four quarters ahead in the U.K. The forecasts are tested to see if they satisfy three implications of the rational expectations hypothesis: unbiasedness, efficiency and consistency. Explicit consideration is given to the information set available when the forecasts are made. In general, the data are consistent with the rational expectations hypothesis and our results provide encouragement for the view that aggregate expectations will meet the ex post requirements of rationality.     

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.     

An empirical investigation of combinations of economic forecasts

This paper examines the effects of combining three econometric and three times-series forecasts of growth and inflation in the U.K. If forecasts are unbiased then a combination exploiting this fact will be more efficient than an unrestricted combination. Ex post econometric forecasts may be biased but ex ante they are unbiased. The results of the study are that a restricted linear combination of the econometric forecasts is superior to an unrestricted combination and also to the unweighted mean of the forecasts. However, it is not preferred to the best of the individual forecasts.     

Are economic forecasts valuable?

A previous study questioned whether the published forecasts of one forecasting organization were valuable to users. A forecast was considered valuable if it differed significantly from a naive model in the sense of predicting the direction of change. The results indicated that it was not possible to show that the one-quarter-ahead predictions were valuable. However, the current-quarter forecasts were valuable. This paper examines whether the results are robust. The forecasts of two additional organizations were examined and the time period was extended. The results are generally robust.     

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.     

Testing weak rationality of forecasts with different time horizons

Starting with conventional tests for weak rationality, we show how additional tests can be performed if predictions with different time horizons are commonly used in them. Next, we show that most of these tests can still be applied if the structure of the economic system changes but only some of them if the true system is unknown. Finally, these tests are applied to the semi-annual one-and two-step predictions of the group of five leading economic research institutes in the Federal Republic of Germany. For the two-step predictions we find more evidence against the rational expectations hypothesis than for the one-step predictions.     

Quarterly liverpool model with current partial information: An exercise in forecasting

This paper applies an algorithm for the solution of partial current information in rational expectation models to the quarterly Liverpool macroeconomic model of the U.K. The algorithm is shown to produce marginally superior results in forecasts both in ex-post and ex-ante forecasts and can be viewed as an additional tool for the forecaster's kit-bag.     

Bayesian consensus forecasts of macroeconomic variables

Economists, like other forecasters, share knowledge, data and theories in common. Consequently, their forecast errors are likely to be highly dependent. This paper reports on an empirical study of 16 macroeconomic forecasters. Composite forecasts are computed using a sequential weighting scheme that takes dependence into account; these are compared to a simple average and median forecasts. A within-sample composite is also calculated. Both these methods perform significantly better than the average or median of the forecasts. This improvement in accuracy is apparently because the dependence between the forecasters' errors is so high that the optimal composite forecasts sometimes lie outside the range of the individual forecasts.     

Judgmental extrapolation and the salience of change

People may often forecast using cognitive procedures that resemble formal time-series extrapolation models. A model of judgmental extrapolation based on exponential smoothing is proposed in which the setting of the trend parameter is hypothesized to depend upon the relative salience of the successive changes. The salience hypothesis was first tested with exponential series by the use of a framing manipulation. As predicted, focusing the subjects' attention on the changes led to more accurate forecasts. In two investment simulation studies, the salience hypothesis was further examined by varying the statistical properties of the price changes. As predicted, subjects were more likely to sell as prices fell and to buy as prices rose (1) as the sample size of similar changes increased; (2) when the variance of the changes was low; and (3) when the absolute value of the mean change was high. Conditions that may influence judgmental forecasting processes are discussed.     

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.     

The leading indicator approach to economic forecasting—retrospect and prospect

Our purpose in this paper is to explain briefly the theory and rationale underlying the leading, coincident and lagging indicators, describe the more important statistical procedures used, and review the evidence on how the indicators have performed in practice. The tests of performance concentrate on data not used in the selection of the indicators, in the United States and nine other countries. We conclude with some suggestions for future research and development, including the application of the approach to the analysis of inflation.     

Seasonality in large-scale macroeconometric models

Macroeconomic indicators are typically appraised in seasonally adjusted form, and forecasts are often presented in a similar way (as annual changes, for example). Moreover, the quarterly macroeconomic models used in forecasting are commonly estimated from seasonally adjusted data. Nevertheless, these models can generate forecasts with seasonal patterns, and this paper assesses the cause and cure of this phenomenon. It is found that forecast seasonality is induced by seasonality in the various inputs: exogenous variables, residual adjustments, the dynamic specification of certain equations, and annual changes in policy variables. Series changing annually but observed quarterly are termed ‘intercalated series’, and are simple examples of periodic processes. Forecast seasonality can be removed by appropriate adjustment of all these inputs. Models containing explicit future expectations variables solved in a model-consistent manner are also considered: numerical sensitivity to the terminal quarter may result from terminal conditions that require adjustment when seasonality is present.     

Collinearity and the use of latent root regression for combining GNP forecasts

In combining economic forecasts a problem often faced is that the individual forecasts display some degree of dependence. We discuss latent root regression for combining collinear GNP forecasts. Our results indicate that latent root regression produces more efficient combining weight estimates (regression parameter estimates) than ordinary least squares estimation (OLS), although out-of-sample forecasting performance is comparable to OLS.     

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.