Comments on “bootstrapping time series models”

Econometric Reviews

Volumn 15, Issue 2, 1996, Pages 169-176

  Politis, Dimitris N ^a   Romano, Joseph P ^b  

^a PURDUE UNIVERSITY   (United States)

^b STANFORD UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 0347440643     PISSN: 07474938     EISSN: 15324168     Source Type: Journal    
DOI: 10.1080/07474939608800347     Document Type: Note

References (13)

1. Beran, R., 1984. Bootstrap methods in statistics. Jber. d. Dt. Math.-Verein, 86: 14–30.
2. Beratil P., Politis D., Romano J., On subsampling estimators with unknown rate of convergence Stanford University, Department of Statistics 1996 Technical Report 476
3. Bickel, P., and Freedman, D., 1981. Some asymptotic theory for the bootstrap. Ann. Statist., 9: 1196–1217.
4. Brockwell, P., and Davis, R., 1991. Time series: theory and methods, New York: Springer.
5. Carlstein, E., 1986. The use of subseries values for estimating the variance of a general statistic from a stationary sequence. Ann. Statist., 14: 1171–1179.
6. Efron, B., 1979. Bootstrap methods: another look at the jackknife. Ann. Statist., 7: 1–26.
7. Ibragimov, I. A., and Rozanov, Y. A., 1978. Gaussian Random Processes, Springer-Verlag.
8. Politis D., Romano J., On the Sample Variance of Linear Statistics Derived from Mixing Sequences 1993 to appear in Stock. Proc. AppL
9. Politis, D., 1994. Large sample confidence regions based on subsamples under minimal assumptions. Ann. Statist., 22: 2031–2050.
10. Politis D., Romano J., Wolf M., Subsampling Nonstationary Time Series Stanford University, Department of Statistics 1996 Technical Report
11. Politis, D., Romano, J., and You, L., 1993. Uniform confidence bands for the spectrum based on subsamples. In Computing Science and Statistics. Proceedings of the 25th Symposiumon the Interface. 1993. pp. 346–351. 25
12. Priestley, M. B., 1981. Spectral Analysis and Time Series, New York: Academic Press.
13. Quenouille, M., 1949. Approximate tests of correlation in time series. J. Royal Statist. Soc, B, 11: 68–84.