Bias-corrected bootstrap prediction intervals for autoregressive model: New alternatives with applications to tourism forecasting

Journal of Forecasting

Volumn 29, Issue 7, 2010, Pages

  Kim, Jae H ^a   Song, Haiyan ^b   Wong, Kevin K F ^b  

^a LA TROBE UNIVERSITY   (Australia)

^b THE HONG KONG POLYTECHNIC UNIVERSITY   (Hong Kong)

Author keywords

Bias correction; Stationarity correction; Time series; Tourist arrivals

Indexed keywords

FACTORIZATION; FORECASTING;

AUTOREGRESSIVE MODELLING; BIAS CORRECTION; INTERVAL FORECASTING; PREDICTION INTERVAL; PROPERTY; SPECTRAL FACTORIZATIONS; STATIONARITY; STATIONARITY-CORRECTION; TIMES SERIES; TOURIST ARRIVAL;

TIME SERIES;

EID: 84863192408     PISSN: 02776693     EISSN: 1099131X     Source Type: Journal    
DOI: 10.1002/for.1150     Document Type: Article

References (24)

1. Box GEP, Jenkins GM. 1970. Time Series Analysis, Forecasting and Control. Holden Day: San Francisco, CA.
2. Chatfield C. 1993. Calculating interval forecasts. Journal of Business and Economic Statistics 11: 121-135.
3. Cho V. 2003. A comparison of three different approaches to tourist arrival forecasting. Tourism Management 24: 323-330.
4. Christoffersen PF. 1998. Evaluating interval forecasts. International Economic Review 39: 841-862.
5. Clements MP, Kim JH. 2007. Bootstrap prediction intervals for autoregressive time series. Computational Statistics and Data Analysis 51: 3580-3594.
6. Clements MP, Taylor N. 2001. Bootstrapping prediction intervals for autoregressive models. International Journal of Forecasting 17: 247-267.
7. Du Preez J, Witt SF. 2003. Univariate versus multivariate time series forecasting: an application to international tourism demand. International Journal of Forecasting 19: 435-451.
8. Efron B, Tibshirani RJ. 1993. An Introduction to the Bootstrap. Chapman & Hall: New York.
9. Goh C, Law R. 2002. Modeling and forecasting tourism demand for arrivals with stochastic nonstationary seasonality and intervention. Tourism Management 23: 499-510.
10. Gustavsson P, Nordström J. 2001. The impact of seasonal unit roots and vector ARMA modelling on forecasting monthly tourism flows. Tourism Economics 7: 117-133.
11. Hall P. 1992. The Bootstrap and Edgeworth Expansion. Springer: New York.
12. Kilian L. 1998a. Small sample confidence intervals for impulse response functions. Review of Economics and Statistics 80: 218-230.
13. Kilian L. 1998b. Confidence intervals for impulse responses under departure from normality. Econometric Reviews 17: 1-29.
14. Kim JH. 1999. Asymptotic and bootstrap prediction regions for vector autoregression. International Journal of Forecasting 15: 393-403.
15. Kim JH. 2001. Bootstrap-after-bootstrap prediction intervals for autoregressive models. Journal of Business and Economic Statistics 19: 117-128.
16. Kim JH. 2004a. Bootstrap prediction intervals for autoregression using asymptotically mean-unbiased parameter estimators. International Journal of Forecasting 20: 85-97.
17. Kim JH. 2004b. Bias-corrected bootstrap prediction regions for vector autoregression. Journal of Forecasting 23: 141-154.
18. Kiviet JF, Phillips GDA. 1994. Bias assessment and reduction in linear error-correction models. Journal of Econometrics 63: 215-243.
19. Poskitt DS, Salau MO. 1993. Stable spectral factorization with applications to the estimation of time series models. Communications in Statistics, Theory and Methods 22: 347-367.
20. Smeral E, Wüger M. 2005. Does complexity matter? Methods for improving forecasting accuracy in tourism: the case of Australia. Journal of Travel Research 44: 100-110.
21. Song H, Li G. 2008. Tourism demand modelling and forecasting: a review of literature. Tourism Management 29: 203-220.
22. Thombs LA, Schucany WR. 1990. Bootstrap prediction intervals for autoregression. Journal of the American Statistical Association 85: 486-492.
23. van Giersbergen NPA. 2005. On the effect of deterministic terms on the bias in stable AR models. Economics Letters 89: 75-82.
24. Witt SF, Witt CA. 1995. Forecasting tourism demand: a review of empirical approach. International Journal of Forecasting 11: 447-475.