No abstract

Eighty-four post-1990 empirical studies of international tourism demand modeling and forecasting using econometric approaches are reviewed. New developments are identified and it is shown that applications of advanced econometric methods improve the understanding of international tourism demand. An examination of the 22 studies which compare forecasting performance suggests that no single forecasting method can outperform the alternatives in all cases. However, the timevarying parameter (TVP) model and structural time series model with causal variables perform consistently well.

No abstract

This study uses meta-analysis to examine the relationship between estimated international tourism demand elasticities and the data characteristics and study features which may affect such empirical estimates. By reviewing 195 studies published during the period 1961-2011, the meta-regression analysis shows that origin, destination, time period, modeling method, data frequency, the inclusion/omission of other explanatory variables and their measures, and sample size all significantly influence the estimates of the demand elasticities generated by a model. Moreover, the demand elasticities at both product and destination levels are generalized by statistically integrating previous empirical estimates. The findings of this meta-analysis will be useful wherever an understanding of the drivers of tourism demand is critically important.

Tourist arrivals and tourist expenditure, in both aggregate and per capita forms, are commonly used measures of tourism demand in empirical research. This study compares these two measures in the context of econometric modelling and the forecasting of tourism demand. The empirical study focuses on demand for Hong Kong tourism by residents of Australia, the UK and the USA. Using the general-to-specific modelling approach, key determinants of tourism demand are identified based on different demand measures. In addition, the forecasting accuracy of these demand measures is examined. It is found that tourist arrivals in Hong Kong are influenced mainly by tourists' income and 'word-of-mouth'/habit persistence effects, while the tourism price in Hong Kong relative to that of the tourist origin country is the most important determinant of tourist expenditure in Hong Kong. Moreover, the aggregate tourism demand models outperform the per capita models, with aggregate expenditure models being the most accurate. The implications of these findings for tourism decision making are that the choice of demand measure for forecasting models should depend on whether the objective of the decision maker is to maximize tourist arrivals or expenditure (receipts), and also that the models should be specified in aggregate form.

Existing non-tourism related literature shows that forecast combination can improve forecasting accuracy. This study tests this proposition in the tourism context by examining the efficiency of combining forecasts based on three different combination methods. The data used for this study relate to tourist arrivals in Hong

Previous research in the area of tourism demand modeling and forecasting has paid little attention to business tourism. This study provides the most comprehensive comparison to date of the accuracy of modern forecasting methods in the context of international business tourism demand forecasting. Seven forecasting models are examined, including the error correction model and various structural time-series and autoregressive integrated moving average (ARIMA) models. The empirical results show that relative forecasting performance is highly dependent on the length of forecasting horizon, that adding explanatory variables to the structural time-series model does not improve forecasting performance, and that testing for unit roots is likely to yield reasonably accurate results under certain conditions.

The vector autoregressive (VAR) modelling technique is used to forecast tourist flows to Macau from eight major origin countries/regions over the period 2003 to 2008. The existing literature shows that the VAR model is capable of producing accurate medium to long term forecasts, and also separate forecasts of the explanatory variables are not required. A further justification for using the VAR technique is that it permits an impulse response analysis to be performed in order to examine the ways in which the demand for Macau tourism responds to the 'shocks' in the economic variables within the VAR system. The implications of this analysis are discussed. The forecasts generated by the VAR models suggest that Macau will face increasing tourism demand by residents from mainland China. Since the needs of Chinese tourists tend to be different from those from other origin countries/regions, especially Western countries, the business sectors in Macau need to pay considerable attention to catering for the needs of Chinese tourists. Dritsakis, N. (2004). Cointegration analysis of German and British tourism demand Frechtling, asting. Butterworth Heinemann: egies, in