Double block bootstrap confidence intervals for dependent data

Biometrika

Volumn 96, Issue 2, 2009, Pages 427-443

  Lee, Stephen M S ^a   Lai, P Y ^a  

^a UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

Coverage calibration; Double block bootstrap; P value; Studentization; Weakly dependent

Indexed keywords

EID: 66249121016     PISSN: 00063444     EISSN: 14643510     Source Type: Journal    
DOI: 10.1093/biomet/asp018     Document Type: Article

References (26)

1. ANDREWS, D. W. K. (1991). Heteroskedasticity and autocorrelation consistent covariance matrix estimation. Econometrica 59, 817-58.
2. ANDREWS, D. W. K. & MONAHANQ, J. C. (1992). An improved heteroskedasticity and autocorrelation consistent covariance matrix estimator. Econometrica 60, 953-66.
3. BERTAIL, P. & POLITIS, D. N. (2001). Extrapolation of subsampling distribution estimators: the i.i.d. and strong mixing cases. Can. J. Statist. 29, 667-80.
4. BHATTACHARYA, R. N. & GHOSH, J. K. (1978). On the validity of the formal Edgeworth expansion. Ann. Statist. 7, 434-51.
5. BU ̈HLMANN, P. (1997). Sieve bootstrap for time series. Bernoulli 3, 123-48.
6. BÜHLMANN, P. (2002). Bootstraps for time series. Statist. Sci. 17, 52-72.
7. CHOI, E. & HALL, P. (2000). Bootstrap confidence regions computed from autoregressions of arbitrary order. J. R. Statist. Soc. B 62, 461-77.
8. D AVISON, A. C. & HALL, P. (1993). On Studentizing and blocking methods for implementing the bootstrap with dependent data. Aust. J. Statist. 35, 215-24.
9. GOTZE, F. & HIPP, C. (1983). Asymptotic expansions for sums of weakly dependent random vectors. Z. Wahr. verw. Geb. 64, 211-39.
10. GÖTZE, F. & Kunscḧ, H. R. (1996). Blockwise bootstrap for dependent observations: higher order approximations for Studentized statistics. Ann. Statist. 24, 1914-33.
11. HALL, P. (1985). Resampling a coverage process. Stoch. Proces. Appl. 19, 259-69.
12. HALL, P. (1992). The Bootstrap and Edgeworth Expansion. New York: Springer.
13. HALL, P., HOROWITZ, J. L. & JING, B.-Y. (1995). On blocking rules for the bootstrap with dependent data. Biometrika 82,561-74.
14. HOROWITZ, J. L., LOBATO, I. N., NANKERVIS, J. C. & SAVIN, N. E. (2006). Bootstrapping the Box-Pierce Q test: a robust test of uncorrelatedness. J. Economet. 133, 841-62.
15. KÜNSCH, H. R. (1989). The jackknife and the bootstrap for general stationary observations. Ann. Statist. 17, 1217-41.
16. LAHIRI, S. N. (1992). Edgeworth correction by 'moving block' bootstrap for stationary and nonstationary data. In Exploring the Limits of Bootstrap, Ed. R. LePage and L. Billard, pp. 183-214. New York: Wiley.
17. LAHIRI, S. N. (2003). Resampling Methods for Dependent Data. New York: Springer.
18. LAHIRI, S. N., FURUKAWA, K. & LEE, Y.-D. (2007). A nonparametric plug-in rule for selecting optimal block lengths for block bootstrap methods. Statist. Methodol. 4, 292-321.
19. LIU, R. Y. & SINGH, K. (1992). Moving blocks jackknife and bootstrap capture weak dependence. In Exploring the Limits of Bootstrap, Ed. R. LePage and L. Billard, pp. 225-48. New York: Wiley.
20. PAPARODITIS, E. & POLITIS, D. N. (2001). Tapered block bootstrap. Biometrika 88, 1105-19.
21. PAPARODITIS, E. & POLITIS, D. N. (2002). The local bootstrap for Markov processes. J. Statist. Plan. Infer. 108, 301-28.
22. POLITIS, D. N., ROMANO, J. P. & WOLF, M. (1997). Subsampling for heteroskedastic time series. J. Economet. 81, 281-317.
23. POLITIS, D. N. & WHITE, H. (2004). Automatic block-length selection for the dependent bootstrap. Economet. Rev. 23, 53-70.
24. RAJARSHI, M. B. (1990). Bootstrap in Markov-sequences based on estimates of transition density. Ann. Inst. Statist. Math. 42, 253-68.
25. ROMANO, J. P. & WOLF, M. (2006). Improved nonparametric confidence intervals in time series regressions. J. Nonparam. Statist. 18, 199-214.
26. ZVINGELIS, J. (2003). On bootstrap coverage probability with dependent data. In Computer-Aided Econometrics, Ed. D. Giles, pp. 69-90. New York: Marcel Dekker.