Some practical aspects of forecasting in organizations

This paper presents the writer's experience, over a period of 25 years, in analysing organizational systems and, in particular, concentrates on the overall forecasting activity. The paper first looks at the relationship between forecasting and decision taking–with emphasis on the fact that forecasting is a means to aid decision taking and not an end in itself. It states that there are many types of forecasting problems, each requiring different methods of treatment. The paper then discusses attitudes which are emerging about the relative advantages of different forecasting techniques. It suggests a model building process which requires‘experience’and‘craftsmanship’, extensive practical application, frequent interaction between theory and practice and a methodology that eventually leads to models that contain no detectable inadequacies. Furthermore, it argues that although models which forecast a time series from its past history have a very important role to play, for effective policy making it is necessary to augment the model by introducing policy variables, again in a systematic not an ‘ad hoc’ manner. Finally, the paper discusses how forecasting systems can be introduced into the management process in the first place and how they should be monitored and updated when found wanting.     

Perspectives on forecasting research in accounting and finance

This paper offers some perspectives on forecasting research in accounting and finance. It is maintained that many common areas of forecasting research exist. Yet, most research has focused upon a particular (Box-Jenkins) technique and a particular (reported earnings) variable, virtually neglecting numerous other relevant forecasting research topics. This symposium issue includes papers which address several of these neglected research topics. The eight papers constituting the issue are classified into three categories: (1) univariate time-series modelling; (2) multivariate time-series modelling; and (3) comparison of experts' forecasts with those of statistical models. Following a summary of the papers, some suggestions for future research are offered.     

Forecasting with an econometric model: The issue of judgemental adjustment

The paper outlines the current state of forecasting with an econometric model. After briefly distinguishing econometric techniques from other statistical approaches and arguing the advantages of this approach the paper concentrates on the issue of judgemental adjustments to models for forecasting purposes. Two types of adjustment are distinguished and the conditions under which each is justified are stated. Guidance in the use of adjustment is offered through a review of considerations in an actual forecasting situation.     

Practitioners' forum. Sales forecasting by forsys

This paper describes a sales forecasting system widely used by European companies. The system, known as FORSYS, includes several unique characteristics which increase its use and applicability among practitioners. FORSYS is simple to use; its underlying rationale is clear to the user; it is adaptive, and it allows the incorporation of special events into the model in order to determine their influence on forecasting.     

Estimation and Prediction Tests of Cash Flow Forecast Accuracy

Recently, analysts' cash flow forecasts have become widely available through financial information services. Cash flow information enables practitioners to better understand the real operating performance and financial stability of a company, particularly when earnings information is noisy and of low quality. However, research suggests that analysts' cash flow forecasts are less accurate and more dispersed than earnings forecasts. We thus investigate factors influencing cash flow forecast accuracy and build a practical model to distinguish more accurate from less accurate cash flow forecasters, using past cash flow forecast accuracy and analyst characteristics. We find significant power in our cash flow forecast accuracy prediction models. We also find that analysts develop cash flow‐specific forecasting expertise and knowhow, which are distinct from those that analysts acquire from forecasting earnings. In particular, cash flow‐specific information is more useful in identifying accurate cash flow forecasters than earnings‐specific information.Copyright © 2011 John Wiley & Sons, Ltd.     

Can forecasting performance be improved by considering the steady state? An application to Swedish inflation and interest rate

This paper investigates whether the forecasting performance of Bayesian autoregressive and vector autoregressive models can be improved by incorporating prior beliefs on the steady state of the time series in the system. Traditional methodology is compared to the new framework—in which a mean‐adjusted form of the models is employed—by estimating the models on Swedish inflation and interest rate data from 1980 to 2004. Results show that the out‐of‐sample forecasting ability of the models is practically unchanged for inflation but significantly improved for the interest rate when informative prior distributions on the steady state are provided. The findings in this paper imply that this new methodology could be useful since it allows us to sharpen our forecasts in the presence of potential pitfalls such as near unit root processes and structural breaks, in particular when relying on small samples. Copyright © 2008 John Wiley & Sons, Ltd.     

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.     

Comparing ex-ante forecasts from a sem and var model: An application to the italian economy

The predictive performance of a large-scale structural econometric model (SEM) of the Italian economy the Prometeia model is compared in this paper with a vector autoregressive (VAR) model estimated for a selection of six main variables of interest. The paper concentrates on the quarterly ex-ante forecasts of GDP growth rate and the annual forecasts of GDP growth and inflation rate, over the period 1980-85. It concludes that no forecaster is systematically better than the other. In particular, the VAR model outperforms the SEM in short-run forecasts, suggesting that, for the latter, more careful attention should be addressed to questions of dynamic specification. On the other hand, for longer intervals, the SEM forecasts are more accurate than the VAR forecasts, in that they can benefit from the judgemental interventions of the model users and the model can pick up the non-linearities of the economy which cannot be captured by the VAR. Given the different kinds of information that can be extracted from the two approaches, it seems more reasonable to consider them as complementary rather than alternative tools for modelling and forecasting. Therefore, rather than attempting to establish the superiority of one type of model over the other, this kind of comparisons should be seen as a useful diagnostic tool for detecting types of model misspecification.     

Testing for unreliable estimators and insignificant forecasts in combined forecasts

The reliability and precision of the weights used in combining individual forecasts, irrespective of the method of combination, is important in evaluating a combined forecast. The objective of this study is not to suggest the ‘best’ method of combining individual forecasts, but rather to propose exploratory procedures, that make use of all available sample information contained in the covariance matrix of individual forecast errors, to (1) detect if the weights used in combining forecasts are ‘reliable’ (and ‘stable’ if it is known that the covariance matrix of forecast errors is stationary over time) and (2) test for ‘insignificant’ individual forecasts used in forming a combined forecast. We present empirical applications using two-year sales and individual forecast data provided by a major consumer durables manufacturer to illustrate the feasibility of our proposed procedures.     

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.     

The teachers/practitioners corner forecasting in business schools: A survey

The study of forecasting techniques has received increased attention in recent years. How to incorporate this topic into the business school curriculum is a frequent subject of discussion. The purpose of this study was to determine whether forecasting is being taught in business schools and how it is incorporated into the curriculum. The survey instrument was sent to 622 member institutions of the American Assembly of Collegiate schools of Business. The importance of teaching forecasting techniques at both the undergraduate and graduate level was investigated.     

A Comparison of Box—Jenkins and objective methods for determining the order of a non-seasonal ARMA Model

This paper evaluates different procedures for selecting the order of a non-seasonal ARMA model. Specifically, it compares the forecasting accuracy of models developed by the personalized Box-Jenkins (BJ) methodology with models chosen by numerous automatic procedures. The study uses real series modelled by experts (textbook authors) in the BJ approach. Our results show that many objective selection criteria provide structures equal or superior to the time-consuming BJ method. For the sets of data used in this study, we also examine the influence of parsimony in time-series forecasting. Defining what models are too large or too small is sensitive to the forecast horizon. Automatic techniques that select the best models for forecasting are similar in size to BJ models although they often disagree on model order.     

Short-run macroeconomic forecasting: The OECD performance

Forecasts for the seven major industrial countries, Canada, France, Germany, Italy, Japan, the United Kingdom and the United States, are published on a regular basis in the OECD's Economic Outlook. This paper analyses the accuracy of the OECD annual forecasts of output and price changes and of the current balance in the balance of payments. As a reference basis, the forecasts are compared with those generated by a naive model, a random walk process. The measures of forecasting accuracy used are the mean-absolute error, the root-mean-square error, the median-absolute error, and Theil's inequality coefficient. The OECD forecasts of real GNP changes are significantly superior to those generated by the random walk process; however, the OECD price and current balance forecasts are not significantly more accurate than those obtained from the naive model. The OECD's forecasting performance has neither improved nor deteriorated over time.     

Factor forecasts for the UK

Data are now readily available for a very large number of macroeconomic variables that are potentially useful when forecasting. We argue that recent developments in the theory of dynamic factor models enable such large data sets to be summarized by relatively few estimated factors, which can then be used to improve forecast accuracy. In this paper we construct a large macroeconomic data set for the UK, with about 80 variables, model it using a dynamic factor model, and compare the resulting forecasts with those from a set of standard time‐series models. We find that just six factors are sufficient to explain 50% of the variability of all the variables in the data set. These factors, which can be shown to be related to key variables in the economy, and their use leads to considerable improvements upon standard time‐series benchmarks in terms of forecasting performance. Copyright © 2005 John Wiley & Sons, Ltd.     

The importance of non-linearities in large forecasting models with stochastic error processes

This paper considers the consequences of the stochastic error process in large non-linear forecasting models. As such models are non-linear, the deterministic forecast is neither the mean nor the mode of the density function of the endogenous variables. Under a specific assumption as to the class of the non-linearity it is shown that the deterministic forecast is actually the vector of marginal medians of the density function. Stochastic simulation techniques are then used to test whether one large forecasting model actually lies within this class.     

The accuracy of extrapolation (time series) methods: Results of a forecasting competition

In the last few decades many methods have become available for forecasting. As always, when alternatives exist, choices need to be made so that an appropriate forecasting method can be selected and used for the specific situation being considered. This paper reports the results of a forecasting competition that provides information to facilitate such choice. Seven experts in each of the 24 methods forecasted up to 1001 series for six up to eighteen time horizons. The results of the competition are presented in this paper whose purpose is to provide empirical evidence about differences found to exist among the various extrapolative (time series) methods used in the competition.     

Practitioners'Forum. Forecasting and Planning the Jeddah Air Traffic with a Mini Model

A simple model of particular socio-economic and technical environment proves useful in forecasting and planning. The specific application forecasts air traffic at King Abdulaziz International Airport (KAIA) in Jeddah, Saudi Arabia, through a number of explanatory variables The purpose of the model is to explain and forecast the change in growth rates of passenger flow through the airport. The dynamics of passenger flow are linked to the dynamics of the oil-based economy of Saudi Arabia and the global economic and business environment.     

Price forecasting and evaluation: An application in agriculture

Because of the high volatility of prices of agricultural commodities over the past decade, the importance of accurate price forecasting for decision makers has become even more acute. This paper reviews literature on forecasting and evaluation. An application with forecasting U.S. hog prices is presented which includes both economic and statistical evaluation measures. Seven forecasting approaches are described and their performances are examined over 24 quarters from 1976 to 1981. These methods include exponential smoothing, an autoregressive integrated moving average process, an econometric model, expert judgement, and a composite forecasting approach. The application gives results which support previous findings in the forecasting literature and suggests that forecasting methods can provide valuable information to the decision maker.     

Econometric modelling for short‐term inflation forecasting in the euro area

This paper examines the problem of forecasting macro‐variables which are observed monthly (or quarterly) and result from geographical and sectorial aggregation. The aim is to formulate a methodology whereby all relevant information gathered in this context could provide more accurate forecasts, be frequently updated, and include a disaggregated explanation as useful information for decision‐making. The appropriate treatment of the resulting disaggregated data set requires vector modelling, which captures the long‐run restrictions between the different time series and the short‐term correlations existing between their stationary transformations. Frequently, due to a lack of degrees of freedom, the vector model must be restricted to a block‐diagonal vector model. This methodology is applied in this paper to inflation in the euro area, and shows that disaggregated models with cointegration restrictions improve accuracy in forecasting aggregate macro‐variables. Copyright © 2007 John Wiley & Sons, Ltd.     

Organizational Pressures on Forecast Evaluation: Managerial,Political, and Procedural Influences

This paper proposes a theory to explain why some forecasting organizations institutionalize forecast accuracy evaluation while others do not. The theory considers internal and external aspects of managerial, political, and procedural factors as they affect forecasting organizations. The theory is then tested using data from a survey of the US Federal Forecasters Group. Though some support for the theory is developed, multiple alternative explanations for results and the ‘public’ nature of the sample organizations prevent wide-scale generalization. The results suggest that larger organizations are more likely to have some form of forecast evaluation than smaller units. The institutionalization of forecast accuracy evaluation is closely linked to internal managerial and procedural factors, while external political pressure tends to reduce the likelihood of institutionalization of evaluation of forecast accuracy.© 1997 John Wiley & Sons, Ltd.