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The need effectively to integrate decision making tasks together with knowledge representation and inference procedures has caused recent research efforts towards the integration of decision support systems with knowledge‐based techniques. Explores the potential benefits of such integration in the area of business forecasting. Describes the forecasting process and identifies its main functional elements. Some of these elements provide the requirements for an intelligent forecasting support system. Describes the architecture and the implementation of such a system, the theta intelligent forecasting information system (TIFIS) that that first‐named author had developed during his dissertation. In TIFIS, besides the traditional components of a decision‐support onformation system, four constituents are included that try to model the expertise required. The information system adopts an object‐oriented approach to forecasting and exploits the forecasting engine of the theta model integrated with automated rule based adjustments and judgmental adjustments. Tests the forecasting accuracy of the information system on the M3‐competition monthly data.

Machine learning methods have recently gained attention in business applications. We will explore the suitability of machine learning methods, particularly support vector regression (SVR) and radial basis function (RBF) approximation, in forecasting company sales. We compare the one-step-ahead forecast accuracy of these machine learning methods with traditional statistical forecasting techniques such as moving average (MA), exponential smoothing, and linear and quadratic trend regression on quarterly sales data of 43 Fortune 500 companies. Moreover, we implement an additive seasonal adjustment procedure on the quarterly sales data of 28 of the Fortune 500 companies whose time series exhibited seasonality, referred to as the seasonal group. Furthermore, we prove a mathematical property of this seasonal adjustment procedure that is useful in interpreting the resulting time series model. Our results show that the Gaussian form of a moving RBF model, with or without seasonal adjustment, is a promising method for forecasting company sales. In particular, the moving RBF-Gaussian model with seasonal adjustment yields generally better mean absolute percentage error (MAPE) values than the other methods on the sales data of 28 companies in the seasonal group. In addition, it is competitive with single exponential smoothing and better than the other methods on the sales data of the other 15 companies in the non-seasonal group.

The liquidity of direct real estate has been surrounded by mystery. Research in the USA and in the UK has contributed much to clarify the liquidity issue of direct real estate. In The Netherlands, not much research exists on this issue; however, a major ALM advisory firm in The Netherlands suggests a liquidity factor of 1.5 times the standard deviation of the ROZ/IPD real estate index, leading to a 50 percent higher risk compared to the current ROZ/IPD real estate index risk. This paper aims to investigate this issue.

The paper investigates whether this is a reasonable assumption by approaching the issue from several perspectives. First, the transaction process, the effects of heterogeneity and the size of the property are reviewed. The market risk between the date of the decision to sell the property and the date on which it was actually sold is also reviewed. The last element reviewed is the reallocation risk, in other words missed opportunities that have arisen because it could take longer to sell property than to sell stocks or bonds. Extensive anonymous information from the main institutional investors in The Netherlands is used, as well as interviews with the main brokers in The Netherlands. The survey is placed in an international context by comparing the results as well as the methods to previous surveys in the UK.

As a result suggestions are presented about risk premiums as a protection against the liquidity risks which turn out to be quite low, much lower than the 50 percent increase of the risk premium on top of the ROZ/IPD real estate index's standard deviation of the total return. The results are compared to risk premiums for stocks and bonds at times of high and average returns.

So far not many surveys have been done on this subject using the bottom up approach. If there were, those have been looked at in the literature review. The unique ROZ/IPD databank allows us to come up with real quantitative results related to the different types of real estate liquidity risks. The paper has identified five of those. The survey is restricted to results in a growing market because of the time frame and it is strongly recommended to repeat it after a depressed market.

In this study, the relative accuracy of four well known methods for forecasting are compared The methods are applied to the time series of earnings per share for a random sample of United States corporations over a lengthy period of time. All the time series exhibit both period‐to‐period movements and seasonal fluctuation. The four models are, (1) Holt‐Winters multiplicative exponential smoothing model, (2) univariate Box‐Jenkins model, (3) linear autoregression of data seasonally adjusted by the Census II–XII method, and (4) linear autoregression of the data seasonally adjusted by the X11‐ARIMA method. The study of financial data of this type is important because (1) these data exhibit time series properties of trend, seasonality, and cycle, (2) earnings per share forecasts are important for purposes of financial planning and investment; and (3) previous studies of this nature were not as exhaustive in terms of the statistical analysis of the results

We investigate the effect standard time series β‐adjustments have on the OLS‐β. We report that most changes are not statistically significant and the β‐adjustments appear to have no relationship to the extent of thin trading. Researchers using β face the difficult choice of using an estimate known to be biased by thin trading, or making an adjustment that may not be statistically significant.

After briefly reviewing the past history of Bayesian econometrics and Alan Greenspan's (2004) recent description of his use of Bayesian methods in managing policy-making risk, some of the issues and needs that he mentions are discussed and linked to past and present Bayesian econometric research. Then a review of some recent Bayesian econometric research and needs is presented. Finally, some thoughts are presented that relate to the future of Bayesian econometrics.

The purpose of the paper is to examine the seasonal structure in the German monetary aggregate M1 and output by means of fractional integration techniques.

The authors use a version of the tests of Robinson that permits testing seasonal I (d) models with the possibility of incorporating seasonal dummy variables and structural breaks.

The results show that there is a strong degree of persistence in their behaviour, especially at the long run or zero frequency, with orders of integration ranging between 1.25 and 1.50.

The main innovation in this work is the use of a new time series approach to the case of seasonality.

Forecasting is a vital part of hospitality operations because it allows businesses to make imperative decisions, such as pricing, promotions, distribution, scheduling, and arranging facilities, based on the predicted demand and supply. This chapter covers three main concepts related to forecasting: it provides an understanding of hospitality demand and supply, it introduces several forecasting methods for practical application, and it explains yield management as a function of forecasting. In the first section, characteristics of hospitality demand and supply are described and several techniques for managing demand and supply are addressed. In the second section, several forecasting methods for practical application are explored. In the third section, yield management is covered. Additionally, examples of yield management applications from airlines, hotels, and restaurants are presented.

In contrast to recent forecasting developments, “Old School” forecasting techniques, such as exponential smoothing and the Box–Jenkins methodology, do not attempt to explicitly model or estimate breaks in a time series. Adherents of the “New School” methodology argue that once breaks are well estimated, it is possible to control for regime shifts when forecasting. We compare the forecasts of monthly unemployment rates in 10 OECD countries using various Old School and New School methods. Although each method seems to have drawbacks and no one method dominates the others, the Old School methods often outperform the New School methods for forecasting the unemployment rates.