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It is proposed that forecasting systems should be implemented on a trial basis and evaluated in terms of accuracy and/or economic benefits prior to full‐scale implementation. The conventional method of evaluating a forecasting system is to compute one or more error terms. Problems occur when no error term can be calculated. A methodology for evaluating a forecasting system under such conditions, based on Bayesian analysis, is put forward. The forecasting system subjected to the evaluation process was intended to improve the catch volume of a salmon fishery. Data for analysis were derived from the activities of two groups, one using the proposed forecasting analysis and one not using it, and the economic consequences associated with each.

This article surveys recent developments in the evaluation of point and density forecasts in the context of forecasts made by vector autoregressions. Specific emphasis is placed on highlighting those parts of the existing literature that are applicable to direct multistep forecasts and those parts that are applicable to iterated multistep forecasts. This literature includes advancements in the evaluation of forecasts in population (based on true, unknown model coefficients) and the evaluation of forecasts in the finite sample (based on estimated model coefficients). The article then examines in Monte Carlo experiments the finite-sample properties of some tests of equal forecast accuracy, focusing on the comparison of VAR forecasts to AR forecasts. These experiments show the tests to behave as should be expected given the theory. For example, using critical values obtained by bootstrap methods, tests of equal accuracy in population have empirical size about equal to nominal size.

Demand forecasting models in companies are often a mix of quantitative models and qualitative methods. As there are so many existing forecasting approaches, many forecasters have difficulty in deciding on which model to select as they may perform “best” in a specific error measure, and not in another. Currently, there is no approach that evaluates different model classes and several interdependent error measures simultaneously, making forecasting model selection particularly difficult when error measures yield conflicting results.

This paper proposes a novel procedure of multi-criteria evaluation of demand forecasting models, simultaneously considering several error measures and their interdependencies based on a two-stage multi-criteria decision-making approach. Analytical Network Process combined with the Technique for Order of Preference by Similarity to Ideal Solution (ANP-TOPSIS) is developed, evaluated and validated through an implementation case of a plastic bag manufacturer.

The results show that the approach identifies the best forecasting model when considering many error measures, even in the presence of conflicting error measures. Furthermore, considering the interdependence between error measures is essential to determine their relative importance for the final ranking calculation.

The paper's contribution is a novel multi-criteria approach to evaluate multiclass demand forecasting models and select the best model, considering several interdependent error measures simultaneously, which is lacking in the literature. The work helps structuring decision making in forecasting and avoiding the selection of inappropriate or “worse” forecasting model.

The paper seeks to evaluate accuracy and efficiency of consensus forecasts for all property rents and total returns in the UK. The aim of the paper is to investigate whether consensus forecasts, containing a high degree of judgement, are better than forecasts produced by uncomplicated time‐series and econometric models that practitioners can easily estimate and use for forecasting.

This study estimates simple models of all property rents and returns and generates forecasts for one‐ and two‐year horizons on a rolling basis over the period 1999 to 2004. These forecasts are real time forecasts. That is they are made using information available to the analyst at the time of the forecast each year and no future knowledge is assumed. The forecasts made by these models are compared with the corresponding consensus forecasts. The comparative assessment is based on conventional tests for bias, variability and efficiency of forecasts.

If attention is focused on rents, the consensus forecast is ranked best for the one‐year horizon but it is outperformed by the regression model and a combination of the statistical models for the two‐year horizon. For the one‐year and two‐year forecasts of total returns a simple regression model with interest rates clearly improves upon the consensus forecasts. There is clear evidence that the consensus forecasts fail to incorporate the information contained in recent interest rate movements. Therefore subject to the sample period for this analysis the survey forecasts of total returns cannot be considered impartial. Analysts should include base rate information into their predictions.

This is one of the few attempts to formally evaluate consensus forecasts in the real estate field and perform a direct comparison with quantitative forecasts. It produces initial evidence suggesting that highly judgemental consensus forecasts do not necessarily outperform quantitative forecasts based on fundamentals. It prompts property forecasters and investors to engage in forecast evaluation and include missing information and past errors in their forecasts.

The purpose of this paper is to investigate the effect of selected organizational factors on the performance of employees charged with sales forecasting, and to compare this across the different organizational environments of Central-Eastern European (CEE) retail chains.

The research involves seven major pan-European retail chain companies, with a total number of 201 respondents. Data were collected via a questionnaire [computer-aided personal interview (CAPI) and human-aided personal interview (HAPI) method] with a five-point scale evaluation of both dependent (organizational factors) and independent (performance indicator) variables. Cluster analysis was then used to derive the characteristics of average organizational environments, and correlation analysis was used to investigate the direction and size of the performance effect.

The results confirmed that different organizational environments have differing effects on the performance of forecasters. It also showed that the “hard core” factors (performance evaluation and information systems) do not have a dominant effect on employee performance in any of the environments regardless of their quality, and are aggregately outclassed by “soft” factors (communication lines and management support). Finally, the research indicated that among the personal attributes related to individual forecasters, domain and forecasting work experience have significant, beneficial effects on forecasting performance, whereas formal education level was detected to have a negative effect and can be, at best, considered as non-contributor.

The research results along with available literature enable us to define four management theses (focus on system, less on people; soft factors are equal to hard ones; higher formal education does not contribute to forecasting performance; and do not overestimate the social and morale situation on the working place) as well as four stages of organizational development, creating a practitioner’s guide to necessary steps to improve an environment’s key factors, i.e. performance evaluation, information systems and forecasting work experience.

Although there are regular studies examining the effect of organizational factors on employee performance, very few have explored this relationship in a forecasting context, i.e. in the case of employees charged with sales forecasting. Furthermore, the paper brings evidence on this topic from the CEE area, which is not covered in most prominent forecasting management studies.

Weak-form rationality of fixed-event forecasts implies that forecast revisions should not be correlated. However, significant positive correlations between consecutive forecast revisions were found in most USDA forecasts for U.S. corn, soybeans, wheat, and cotton. This study developed a statistical procedure for correction of this inefficiency which takes into account the issue of outliers, the impact of forecast size and direction, and the stability of revision inefficiency. Findings suggest that the adjustment procedure has the highest potential for improving accuracy in corn, wheat, and cotton production forecasts.

Fuel demand forecast is a fundamental tool to guide private planning actions and public policies aim to guarantee energy supply. This paper aims to evaluate different forecasting methods to project the consumption of light fuels in Brazil (fuel used by vehicles with internal combustion engine).

Eight different methods were implemented, besides of ensemble learning technics that combine the different models. The evaluation was carried out based on the forecast error for a forecast horizon of 3, 6 and 12 months.

The statistical tests performed indicated the superiority of the evaluated models compared to a naive forecasting method. As the forecast horizon increase, the heterogeneity between the accuracy of the models becomes evident and the classification by performance becomes easier. Furthermore, for 12 months forecast, it was found methods that outperform, with statistical significance, the SARIMA method, that is widely used. Even with an unprecedented event, such as the COVID-19 crisis, the results proved to be robust.

Some regulation instruments in Brazilian fuel market requires the forecast of light fuel consumption to better deal with supply and environment issues. In that context, the level of accuracy reached allows the use of these models as tools to assist public and private agents that operate in this market.

The study seeks to fill a gap in the literature on the Brazilian light fuel market. In addition, the methodological strategy adopted assesses projection models from different areas of knowledge using a robust evaluation procedure.

The purpose of this paper is to compare the daily conditional variance forecasts of seven GARCH-family models. This paper investigates whether the advanced GARCH models outperform the standard GARCH model in forecasting the variance of stock indices.

Using the daily price observations of 21 stock indices of the world, this paper forecasts one-step-ahead conditional variance with each forecasting model, for the period 1 January 2000 to 30 November 2013. The forecasts are then compared using multiple statistical tests.

It is found that the standard GARCH model outperforms the more advanced GARCH models, and provides the best one-step-ahead forecasts of the daily conditional variance. The results are robust to the choice of performance evaluation criteria, different market conditions and the data-snooping bias.

This study addresses the data-snooping problem by using an extensive cross-sectional data set and the superior predictive ability test (Hansen, 2005). Moreover, it covers a sample period of 13 years, which is relatively long for the volatility forecasting studies. It is one of the earliest attempts to examine the impact of market conditions on the forecasting performance of GARCH models. This study allows for a rich choice of parameterization in the GARCH models, and it uses a wide range of performance evaluation criteria, including statistical loss functions and the Mince-Zarnowitz regressions (Mincer and Zarnowitz 1969). Therefore, the results are more robust and widely applicable as compared to the earlier studies.

Virgin Atlantic Cargo is one of the largest air freight operators in the world. As part of a wider strategic development initiative, the company has identified forecasting accuracy as of strategic importance to its operational efficiency. This is because accurate forecast enables the company to have the right resources available at the right place and time. The purpose of this paper is to undertake an evaluation of current month-to-date forecasting utilized by Virgin Atlantic Cargo. The study employed demand patterns drawn from historical data on chargeable weight over a seven-year-period covering six of the company's routes.

A case study is carried out, where a comparison between forecasting models is undertaken using error accuracy measures. Data in the form of historical chargeable weight over a seven-year-period covering six of the company's most profitable routes are employed in the study. For propriety and privacy reasons, data provided by the company have been sanitized.

Preliminary analysis of the time series shows that the air cargo chargeable weight could be difficult to forecast due to demand fluctuations which appear extremely sensitive to external market and economic factors.

The study contributes to existing literature on air cargo forecasting and is therefore of interest to scholars examining the problems of overbooking. Overbooking which is employed by air cargo operators to hedge against “no-show” bookings. However, the inability of air cargo operators to accurately predict cargo capacity unlikely to be used implies that operators are unable to establish with an aspect of certainty their revenue streams. The research methodology adopted is also predominantly discursive in that it employs a synthesis of existing forecasting literature and real-life data for accuracy analysis.

Over a decade ago, Bretschneider and Gorr proposed two directions for future research in government forecasting: one was to extend the research on developing and evaluating alternative forecasting methods and the other, to look at forecasting as a human activity and examine how organizational factors affect forecasting. What has happened since then? To see partially what has been done and what remains to be done, this paper provides a review of the literature on government revenue forecasting with a primary focus on the state level and identifies areas for future research in government revenue forecasting.