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Planning, both “operational” and “strategic”, relies on accurate forecasting. Planning in tourism is no less dependent on accurate forecasts. However, tourism demand forecasting has been dominated by the application of regression/econometric techniques. Past studies on the forecasting accuracy of econometric/regression models suggest that forecasts generated by these models are not necessarily superior to forecasts generated by simple time series techniques. Seven time series forecasting techniques were used to generate forecasts of international tourist arrivals from Thailand to Hong Kong. The results confirm that simple techniques may be just as accurate and often more time‐and cost‐effective than more complex ones. Practitioners in the tourism industry may confidently use any of the forecasting techniques demonstrated here for their short‐term planning activities.

During recent years a number of techniques have been developed to aid in the forecasting of corporate sales, individual product demand, economic indicators, and other related series. These techniques have included classical time series analysis, multiple regression and adaptive forecasting procedures. As a result of these developments, the individual company and decision maker is faced with the task of selecting the forecasting technique that is most appropriate for his situation. This article reports research conducted at INSEAD on how simulation can be used to compare and evaluate alternative forecasting techniques for a specific application.

The importance of accurate forecasts of tourism demand for managerial decision making is widely recognized (see, for example, Archer 1987), and this study examines the literature on the accuracy of tourism forecasts generated by different forecasting techniques. In fact, although there are many possible forecasting methods, in practice relatively few of these have been used for tourism forecasting.

The aim of this paper is to investigate the effect of management earnings forecasts on the level of information asymmetry around subsequent earnings announcement.

Employing the adverse selection cost method suggested by George et al., the paper compares for each sample firm the adverse selection cost around earnings announcement in forecasting years with that in non‐forecasting years.

Consistent with Diamond and Verrecchia is the finding that the earnings announcement in non‐forecasting years decreases information asymmetry during a three‐day announcement period and increases in a post‐announcement period up to seven days. No significant change in information asymmetry between pre‐ and post‐announcement periods when firms released a “good” news forecast is found. The firms that previously released a “bad” news forecast experience a significantly lower information asymmetry than those that did not forecast during announcement or post‐announcement days, and experience a decrease in information asymmetry in a five to seven‐day post‐announcement period.

This paper provides the first empirical reports on the different information asymmetry changes around earnings announcements followed by a “good” news management forecast from those followed by a “bad” news forecast.

The purpose of this article is to provide a critique of SAP's enterprise resource planning (ERP) (release ECC 6.0) forecasting functionality and offer guidance to SAP practitioners on overcoming some identified limitations.

The SAP ERP forecasting functionality is reviewed against prior seminal empirical business forecasting research.

The SAP ERP system contains robust forecasting methods (exponential smoothing), but could be substantially improved by incorporating simultaneous forecast comparisons, prediction intervals, seasonal plots and/or autocorrelation charts, linear regressions lines for trend analysis, and event management based on structured judgmental forecasting or intervention analysis.

The findings provide guidance to SAP forecasting practitioners for improving forecast accuracy via important forecasting steps outside of the system.

The paper contributes to the need for studies of widely adopted ERP systems to critique vendor claims and validate functionality through prior empirical research, while offering insights and guidance to SAP's 12 million+ worldwide enterprise system practitioners.

The purpose of this article is to examine empirically the impact of aggregation on forecasting accuracy; specifically, whether more accurate forecasts are obtained by forecasting a number of disaggregated tourist flows and summing the forecasts to obtain the aggregate forecast, or by summing the disaggregated tourist flows and forecasting the aggregate series directly. On the one hand, it may be easier to produce accurate forecasts from disaggregated series as the latter allow for differing behavioural patterns which may be more readily recognisable and hence easier to model and extrapolate. On the other hand, more aggregate series may be less susceptible to “noise” and therefore easier to forecast.

The purpose of this paper is to evaluate accuracy of macro fiscal forecasts done by Government of Zimbabwe and the spillover effects of forecasting errors over the period 2010-2015.

In line with the study objectives, the study employed the root mean square error methodology to measure the accuracy of macro fiscal forecasts, borrowing from the work of Calitz et al. (2013). The spillover effects were assessed through running simple regression in Eviews programme. The data used in the analysis are based on annual national budget forecasts presented to the Parliament by the Minister of Finance. Actual data come from the Ministry of Finance budget outturns and Zimbabwe Statistical Agency published national accounts.

The results of the root mean square error revealed relatively high levels of macro-fiscal forecasting errors, with revenue recording the highest. The forecasting errors display a tendency of under predicting the strength of economic recovery during boom and over predicting its strength during periods of weakness. The study although found significant evidence of GDP forecasting errors translating into revenue forecasting inaccuracies, the GDP forecasting errors fail to fully account for the revenue errors. Revenue errors were, however, found to be positive and significant in explaining the budget balance errors.

In other jurisdictions, particularly developed countries, they undertake regular evaluation of their forecasts in order to improve their forecasting procedures, which translate into quality public service delivery. The situation is lagging in Zimbabwe. Given the poor performance in public service delivery in Zimbabwe, this study contributes in dissecting the sources of the challenge by providing a comprehensive review of macro fiscal forecasts.

This study is motivated by the view that Democrats are concerned with reducing unemployment in the short‐run, while Republicans are concerned with keeping inflation low to promote economic stability and growth. The purpose of this paper is to ask whether the Federal Reserve forecasts of nonfarm payroll employment are accurate and free of systematic bias during the 1977‐2000 Democratic and Republican administrations.

The authors employ comparable forecasts from a univariate autoregressive integrated moving‐average (ARIMA) model to assess forecast accuracy. An ARIMA model efficiently utilizes past information and thus yields desirable forecasts commonly used as benchmarks.

Federal Reserve forecasts during the Democratic Administrations, while failing to outperform the ARIMA forecasts, display systematic under‐prediction. Such evidence is consistent with a discretionary approach to monetary policy when the bias is in the direction of full employment. During the Republican Administrations, the Federal Reserve forecasts, while superior to the ARIMA forecasts, are free of systematic bias. This, we argue, is consistent with a monetary policy that approximated the Taylor rule.

The distinct behavior over the two administrations is unique to the nonfarm payroll employment forecasts and cannot be substantiated for the Federal Reserve forecasts of other macroeconomic variables.

This study provides new insights into the monetary policies pursued during the 1977‐2000 Democratic and Republican administrations. The findings are useful and informative for the design and implementation of monetary policy.

Forecasting is a natural part of human behaviour and experience whether we explicitly realise it or not. For all of us are continually implementing plans as we go about our daily living and these plans must necessarily be founded upon views about the future i.e. upon forecasts. These forecasts may, of course, be very imprecise and it may be argued that to dignify such an imprecise and unstructured process as ‘forecasting’ is to misuse the term. Then again we may rely for our main forecasts on views of experts such as weathermen, modifying them to suit our own particular needs. What is true for the individual is also true for the firm in its approach to forecasting. It may be very imprecise and unstructured and a good deal of reliance may be placed on the views of experts such as economic forecasters. However, the purpose of this article is to argue that forecasting may be conducted in a systematic manner, despite all the pitfalls to be enumerated, and to explain the growth of the forecasting activity in industry and its role.

The ability to forecast consumer demand accurately is of great importance to companies in the consumer market. The food industry, in particular, views consumer availability as the cornerstone of their business. However, many companies concede that their forecasting process does not perform as well as they would wish. A group of forecasting and demand managers from some of the leading UK food companies, with the support of Leatherhead Food RA, examined the problems associated with their functions over an 18‐month period. This paper presents the key findings from their collaborative work.