Leading indicator tourism forecasts

Tourism Management

Volumn 24, Issue 5, 2003, Pages 503-510

  Kulendran, Nada ^a   Witt, Stephen F ^b  

^a VICTORIA UNIVERSITY   (Australia)

^b UNIVERSITY OF SURREY   (United Kingdom)

Author keywords

ARIMA; Error correction model; Forecast accuracy; Leading indicator; Transfer function

Indexed keywords

ERROR CORRECTION; FORECASTING METHOD; METHODOLOGY; TOURISM MARKET;

UNITED KINGDOM;

EID: 0141906215     PISSN: None     EISSN: 02615177     Source Type: Journal    
DOI: 10.1016/S0261-5177(03)00010-4     Document Type: Article

References (21)

1. Edlund, P., & Karlsson, S. (1993). Forecasting the Swedish unemployment rate: Var vs. transfer function modelling. International Journal of Forecasting, 9, 61-76.
2. Engle, R. F., Granger, C. W. J., & Hallman, J. J. (1989). Merging short- and long-run forecasts: An application of seasonal cointegration to monthly electricity sales forecasting. Journal of Econometrics, 40, 45-62.
3. Estrella, A., & Mishkin, F. S. (1998). Predicting US recessions: Financial variables as leading indicators. The Review of Economics and Statistics, 80(1), 45-61.
4. Haugh, L. D. (1976). Checking the independence of two covariance-stationary time series: A univariate residual cross-correlation approach. Journal of the American Statistical Association, 71, 378-385.
5. Hylleberg, S., Engle, R. F., Granger, C. W. J., & Yoo, B. S. (1990). Seasonal integration and cointegration. Journal of Econometrics, 44, 215-238.
6. Johansen, S. (1988). Statistical analysis of cointegration vectors. Journal of Economic Dynamics and Control, 12, 231-254.
7. Johansen, S., & Juselius, K. (1990). Maximum likelihood estimation and inference on cointegration with application to the demand for money. Oxford Bulletin of Economics and Statistics, 52, 169-210.
8. Kulendran, N., & Witt, S. F. (2001). Cointegration versus least squares regression. Annals of Tourism Research, 28, 291-311.
9. Lahiri, K., & Moore, G. H. (1991). Leading economic indicators. New approaches and forecasting records. Cambridge: Cambridge University Press.
10. Makridakis, S., Wheelwright, S. C., & McGee, V. E. (1989). Forecasting: Methods and applications (2nd ed.). New York: Wiley.
11. Pesaran, M. H., & Pesaran, B. (1997). Working with Microfit 4.0: Interactive econometric analysis. Oxford: Oxford University Press.
12. Qi, M. (2001). Predicting US recessions with leading indicators via neural network models. International Journal of forecasting, 17, 383-401.
13. Rossello-Nadal, J. (2001). Forecasting turning points in international visitor arrivals in the Balearic Islands. Tourism Economics, 7, 365-380.
14. Sheldon, P. J., & Var, T. (1985). Tourism forecasting: A review of empirical research. Journal of Forecasting, 4, 183-195.
15. Song, H., & Witt, S. F. (2000). Tourism demand modelling and forecasting: Modern econometric approaches. Oxford: Pergamon.
16. Turner, L. W., Kulendran, N., & Fernando, H. (1997). The use of composite national indicators for tourism forecasting. Tourism Economics, 3, 309-317.
17. Turner, L. W., & Witt, S. F. (2001). Forecasting tourism using univariate and multivariate structural time series models. Tourism Economics, 7, 135-147.
18. Weller, B. R. (1990). Predicting small region sectoral responses to changes in aggregate economic activity: A time series approach. Journal of Forecasting, 9, 273-281.
19. Witt, S. F., & Witt, C. A. (1992). Modeling and forecasting demand in tourism. London: Academic Press.
20. Witt, S. F., & Witt, C. A. (1995). Forecasting tourism demand: A review of empirical research. International Journal of Forecasting, 11, 447-475.
21. Wong, K. K. F. (1997). The relevance of business cycles in forecasting international tourist arrivals. Tourism Management, 18, 581-586.