Introducing model uncertainty in time series bootstrap

Statistica Sinica

Volumn 14, Issue 1, 2004, Pages 155-174

  Alonso, Andrés M ^a   Peña, Daniel ^b   Romo, Juan ^b  

^a UNIVERSIDAD AUTÓNOMA DE MADRID   (Spain)

^b UNIVERSIDAD CARLOS III DE MADRID   (Spain)

Author keywords

Model uncertainty; Prediction; Sieve bootstrap; Time series

Indexed keywords

EID: 1842487331     PISSN: 10170405     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (34)

1. Akaike, H. (1969). Fitting autoregressive models for prediction. Ann. Inst. Statist. Math. 21, 243-247.
2. Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. In 2nd International Symposium on Information Theory (Edited by B. N. Petrov and F. Csaki), 267-281. Akademiai Kiado, Budapest.
3. Alonso, A. M., Peña, D. and Romo, J. (2002). Forecasting time series with sieve bootstrap. J. Statist. Plann. Inference 100, 1-11.
4. Alonso, A. M., Peña, D. and Romo, J. (2003). On introducing model uncertainty in time series bootstrap. Technical Report, Universidad Autónoma de Madrid, Madrid. http://www.uam.es/andres.alonso/Papeles/ss2.pdf
5. Bhansali, R. J. (1983). A simulation study of autoregressive and window estimators of the inverse autocorrelation function. Appl. Statist. 32, 141-149.
6. Bhansali, R. J. (1993). Order selection for linear time series models: A review. In Developments in Time Series Analysis (Edited by T. Subba Rao), 50-66. Chapman and Hall, London.
7. Box, G. E. P., Jenkins, G. M. and Reinsel, G. (1994). Time Series Analysis: Forecasting and Control. Prentice-Hall, San Francisco.
8. Breidt, F. J., Davis, R. A. and Dunsmuir, W. T. M. (1995). Improved bootstrap prediction intervals for autoregressions. J. Time Ser. Anal. 16, 177-200.
9. Buckland, S. T., Burnham, K. P. and Augustin, N. H. (1997). Model selection: an integral part of inference. Biometrics 53, 603-618.
10. Bühlmann, P. (1997). Sieve bootstrap for time series. Bernoulli 3, 123-148.
11. Cao, R., Febrero-Bande, M., González-Manteiga, W., Prada-Sánchez, J. M. and García-Jurado, I. (1997). Saving computer time in constructing consistent bootstrap prediction intervals for autoregressive processes. Comm. Statist. Simulation Comput. 26, 961-978.
12. Grigoletto, M. (1998). Bootstrap prediction intervals for autoregresions: some alternatives. Internat. J. Forecasting 14, 447-456.
13. Hjorth, J. S. U. (1994). Computer Intensive Statistical Methods: Validation, Model Selection, and Bootstrap. Chapman and Hall, London.
14. Hurvich, C. M. and Tsai, C. L. (1989). Regression and time series model selection in small samples. Biometrika 76, 297-307.
15. Hurvich, C. M. and Tsai, C. L. (1991). Bias of the corrected AIC criterion for underfitted regression and time series models. Biometrika 78, 499-509.
16. Inoue, A. and Kilian, L. (2002). Bootstrapping smooth functions of slope parameters and innovations variance in VAR(∞) models. Internat. Econom. Rev. 43, 309-331.
17. Karagrigoriou, A. (1997). Asymptotic efficiency of the order selection of a nongaussian AR process. Statist. Sinica 7, 407-423.
18. Kass, R. E. and Raftery, A. E. (1995). Bayes factors. J. Amer. Statist. Assoc. 90, 773-795.
19. Kass, R. E. and Wasserman, L. (1995). A reference Bayesian test for nested hypotheses and its relationship to the Schwarz criterion. J. Amer. Statist. Assoc. 90, 928-934.
20. Kilian, L. (1998). Accounting for lag uncertainty in autoregressions: The endogenous lag order bootstrap algorithm. J. Time Ser. Anal. 19, 531-548.
21. Kreiss, J.-P. (1988). Asymptotical Inference of Stochastic Processes. Habilitationsschrift, Universität Hamburg.
22. Kreiss, J.-P. (1992). Bootstrap procedures for AR(∞) processes. In Bootstrapping and Related Techniques (Edited by K.-H. Jöckel, G. Rothe and W. Sendler), 107-113. Springer-Verlag, Berlin.
23. LeBlanc, M. and Tibshirani, R. (1996). Combining estimates in regresion and classification. J. Amer. Statist. Assoc. 91, 1641-1650.
24. Masarotto, G. (1990). Bootstrap prediction intervals for autoregressions. Internat. J. Forecasting 6, 229-239.
25. Paparoditis, E. (1996). Bootstrapping autoregressive and moving average parameter estimates of infinite order vector autoregressive processes. J. Multivariate Anal. 57, 277-296.
26. Pascual, L., Romo, J. and Ruiz, E. (2001). Effects of parameter estimation on prediction densities: a bootstrap approach. Internat. J. Forecasting 17, 83-103.
27. Peña, D. and Maravall, A. (1991). Interpolation, outliers, and inverse autocorrelations. Comm. Statist. Theory Methods 20, 3175-3186.
28. Pötscher, B. M. (1990). Estimation of autoregressive moving-average order given an infinite number of models and approximation of spectral densities. J. Time Ser. Anal. 11, 165-179.
29. Stine, R. A. (1987). Estimating properties of autoregressive forecasts. J. Amer. Statist. Assoc. 82, 1072-1078.
30. Thombs, L. A. and Schucany, W. R. (1990). Bootstrap prediction intervals for autoregression. J. Amer. Statist. Assoc. 85, 486-492.
31. Schwarz, G. (1978). Estimating the dimension of a model. Ann. Statist. 6, 461-464.
32. Shibata, R. (1976). Selection of the order of an autoregressive model by Akaike's information criterion. Biometrika 63, 117-126.
33. Shibata, R. (1980). Asymptotically efficient selection of the order of the model for estimating parameters of a linear process. Ann. Statist. 8, 147-164.
34. Zhang, P. (1993). On the convergence rate of model selection criteria. Comm. Statist. Theory Methods 22, 2765-2775.