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 dgerences found to exist among the various extrapolative (time series) methods used in the competition. KEYWORDS Forecasting Time series Evaluation AccuracyComparison Empirical studyForecasting is an essential activity both at the personal and organizational level. Forecasts can be obtained by:(a) purely judgemental approaches; It is important to understand that there is no such thing as the best approach or method as there is no such thing as the best car or best hi-fi system. Cars or hi-fis differ among themselves and are bought by people who have different needs and budgets. What is important, therefore, is not to look for 'winners' or 'losers', but rather to understand how various forecasting approaches and methods differ from each other and how information can be provided so that forecasting users can be able to make rational choices for their situation.Empirical studies play an important role in better understanding the pros and cons of the various forecasting approaches or methods (they can be thought of as comparable to the testsconducted by consumer protection agencies when they measure the characteristics of various products).In forecasting, accuracy is a major, although not the only factor (see note by Carbone in this issue of the Journal of Forecasring) that has been dealt with in the forecasting literature by empirical or experimental studies. Summaries of the results of published empirical studies dealing with accuracy can be found in Armstrong (1978), Makridakis and Hibon (1979), and Slovic (1972). The general conclusions from these three papers are: (a) Judgemental approaches are not necessarily more accurate than objective methods: (b) Causal or explanatory methods are not necessarily more accurate than extrapolative methods: and (c) More complex or statistically sophisticated methods are not necessarily more accurate than simpler methods. The present paper is another empirical study concerned mainly with the post-sample forecasting accuracy of extrapolative (time series) methods. The study was organized as a 'forecasting competition' in which expert participants analysed and forecasted many real life time series.This paper extends and enlarges the study by Makridakis and Hibon (1979). The major differences between the present and the previous study owe their origins to suggestions made during a discussion of the previous study at a meeting of the Royal Statistical Society (see Makridakis and Hibon. 1979) and in privat...

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Accurate forecasts are crucial to successful planning in many organizations and in 2001 forty international experts published a set of principles to guide best practice in forecasting. Some of the principles relate to the use management judgment. Almost all organisations use judgment at some stage in their forecasting process, but do they do so effectively? While judgment can lead to significant improvements in forecasting accuracy, it can also suffer from biases and inconsistency. The principles therefore

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Macroeconomic forecasts are used extensively in industry and government The historical accuracy of US and UK forecasts are examined in the light of different approaches to evaluating macro forecasts. Issues discussed include the comparative accuracy of macroeconometric models compared to their time series alternatives, whether the forecasting record has improved over time, the rationality of macroeconomic forecasts and how a forecasting service should be chosen. The role of judgement in producing the forecasts is also considered where the evidence unequivocally favors such interventions. Finally the use of macroeconomic forecasts and their effectiveness is discussed. The conclusion drawn is that researchers have paid too little attention to the issue of improving the forecasting accuracy record. Areas where improvements would be particularly valuable are highlighted.Keywords: econometric models, macroeconomic forecasting, rationality, forecast evaluation, structural breaks, cliometrics, industry structure Considerable intellectual activity within the economics profession is devoted to the production, interpretation and analysis of forecasts of major economic variables. Since such forecasts are important to both government planning and industry, it is material to determine how well we, as a profession, have performed this activity and what lessons may lead to improvements. Given the number of analyses that have included predictions of one economic variable or another and the range and depth of the macroeconomic forecasting industry (as surveyed by Fildes, 1995), it is impossible, in one paper, to review all aspects of the field. We will, therefore, concentrate on surveying and analyzing the predictions of short-run GDP (GNP), with particular emphasis upon real GDP and inflation forecasts for the US and the UK, bringing in data from other countries only when necessary in order to have a common set of information about cyclical conditions, etc. We focus on GDP and inflation forecasts because these much predicted variables are of interest to the entire profession. Unfortunately, this emphasis precludes an analysis of the characteristics of the forecasts of other variables and leaves many questions unanswered, e.g. which GDP components were the hardest to predict and which contributed the most to the inaccuracy of the aggregate forecasts?While many techniques have been used to make short-run GDP forecasts, the focus of this article will be on quantitative methods. In examining methods designed to provide quantitative estimatesThe State of Macroeconomic Forecasting 2 of GDP, we will consider time series analyses, econometric models (as well as forecasts made using judgmental techniques), and the contribution that expert judgment has made to the modeling process. As the survey evidence in Fildes (1995, p.6) showed, this range of methods covers those used by macroeconomic forecasting services. Since other articles have compared the structure of many of the econometric models used in macroeconomic forecasting, f...

In marketing analytics applications in OR, the modeler often faces the problem of selecting key variables from a large number of possibilities. For example, SKU level retail store sales are affected by inter and intra category effects which potentially need to be considered when deciding on promotional strategy and producing operational forecasts, but no research has put this well accepted concept into forecasting practice: an obvious obstacle is the ultra-high dimensionality of the variable space. This paper develops a four steps methodological framework to overcome the problem. It is illustrated by investigating the value of both intra-and inter-category SKU level promotional information in improving forecast accuracy. The method consists of the identification of potentially influential categories, the building of the explanatory variable space, variable selection and model estimation by a multistage LASSO regression, and the use of a rolling scheme to generate forecasts. The success of this new method for dealing with high dimensionality is demonstrated by improvements in forecasting accuracy compared to alternative methods of simplifying the variable space. The empirical results show that models integrating more information perform significantly better than the baseline model when using the proposed methodology framework. In general, we can improve the forecasting accuracy by 14.3 percent over the model using only the SKU's own predictors. But of the improvements achieved, 88.1 percent of it comes from the intra-category information, and only 11.9 percent from the inter-category information. The substantive marketing results also have implications for promotional category management.

From its foundation, operational research (OR) has made many substantial contributions to practical forecasting in organizations. Equally, researchers in other disciplines have influenced forecasting practice. Since the last survey articles in JORS, forecasting has developed as a discipline with its own journals. While the effect of this increased specialization has been a narrowing of the scope of OR's interest in forecasting, research from an OR perspective remains vigorous. OR has been more receptive than other disciplines to the specialist research published in the forecasting journals, capitalizing on some of their key findings. In this paper, we identify the particular topics of OR interest over the past 25 years. After a brief summary of the current research in forecasting methods, we examine those topic areas that have grabbed the attention of OR researchers: computationally intensive methods and applications in operations and marketing. Applications in operations have proved particularly important, including the management of inventories and the effects of sharing forecast information across the supply chain. The second area of application is marketing, including customer relationship management using data mining and computer-intensive methods. The paper concludes by arguing that the unique contribution that OR can continue to make to forecasting is through developing models that link the effectiveness of new forecasting methods to the organizational context in which the models will be applied. The benefits of examining the system rather than its separate components are likely to be substantial.