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The objective of this study is to develop a set of econometric models for use in forecasting international tourist demand, as represented by the number of tourist visits, from the F.R. Germany and the United Kingdom to their respective major destinations.

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 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.

Implications of Modern Portfolio Theory for Investment Management. The general principles of portfolio management are explained by Dobbins and Witt, Sprecher, Francis, Van Home and Fama and Miller. Portfolio theory is concerned with the choice of efficient combinations of assets and its foundation lies in the work of Markowitz. It is assumed that investors base their decisions simply on the expected return and variance of return of assets, where the variance is taken to measure risk. For any given level of risk, the optimal portfolio is that which offers the maximum expected return; and for any given expected return, the investor prefers minimum risk. The set of efficient portfolios therefore comprises those combinations of assets which promise the highest expected return corresponding to each level of risk.

Considerable attention has been devoted in the tourism literature to the construction and estimation of models explaining the demand for tourism (for a comprehensive review of such models see Witt and Martin 1989). Such studies have, in general, focused upon holiday tourism or total tourism, with scant attention being paid to explaining the demand for congress tourism.

The “causal model” approach to business forecasting attempts to discover, mainly on the basis of economic knowledge, those variables which appear to have influenced the forecast variable in the past, and then estimates by econometric methods the quantitative relationship between the causal and forecast variables. Future values of the forecast variable are then obtained by using forecasts of the causal variables in conjunction with the estimated relationship.

Tourism plays an important role in the Portuguese economy. It employs 3.5 per cent of the working population and is the most rapidly growing economic activity in Portugal (OECD, 1986). Mendonsa (1983, p.218) notes that Portugal ‘is a poor country which has only managed to maintain a favorable balance of trade because of tourism and the remittances of its many emigrants working abroad’. Portuguese tourism is also increasingly successful compared to other destinations; Kendell and de Haast (1986, p. 12) point out that Portugal ‘has one of the fastest growing tourism sectors in Western Europe’.

There are two distinct types of forecasting model in which past experience is used as an indicator of the future, and these may be termed “causal” models and “non‐causal” models. (An extensive discussion of these model types appears in Robinson and Wood and Fildes.) Non‐causal (naive) models simply extrapolate past history on the forecast variable and disregard those forces which caused the particular pattern for the time series. The object is to select the type of curve which provides the closest fit to a given historical series, and complex statistical procedures exist for carrying out this exercise. The great problem with forecasting by extrapolation is that it presupposes that the factors which were the main cause of growth in the past will continue to be the main cause in the future, which may be incorrect, and if this is the case the use of this technique will result in poor forecasts. If one considers sales of foreign holidays as an example, one realises that there may be significant changes in the variables affecting these sales, such as income changes, fare changes, and changes in exchange rates. In order to forecast sales of foreign holidays reasonably accurately it is therefore necessary to construct a causal model in which sales are explicitly related to the determining forces.

Productivity growth in service industries has generally tended to be lower than in manufacturing industries, and the hotel sector is no exception. Problems of measuring productivity are discussed, together with specific reasons for low productivity in the hotel sector. It is suggested that increased usage of operations management techniques by hotel management is likely to result in improved productivity, and various examples are presented of situations in which these techniques can be successfully employed.

When the theme of the conference was first announced, the term ‘Thermalisme’ was used. We wondered, ‘What is Thermalisme?’ We looked in an English dictionary, but without success ‐ the term was not given. So we started looking further afield. We thought that thermalisme suggests thermal, which in turn lead us to think of thermal springs, which implies spas ‐ perhaps' Eventually we found an article by Cohen (1974) which asks the question ‘Who Is A Tourist?’ In this article, Cohen lists eight ‘partial tourist rôles’ (p. 541), one of which is: