Previous |  Up |  Next

Article

Summary:
The first-order autoregression model with heteroskedastic innovations is considered and it is shown that the classical bootstrap procedure based on estimated residuals fails for the least-squares estimator of the autoregression coefficient. A different procedure called wild bootstrap, respectively its modification is considered and its consistency in the strong sense is established under very mild moment conditions.
References:
[1] Basawa I. V., Mallik A. K., McCormick W. P., Taylor R. L.: Bootstrapping explosive autoregressive processes. Ann. Statist. 17 (1989), 1479–1486 DOI 10.1214/aos/1176347376 | MR 1026294 | Zbl 0694.62038
[2] Basu A. K., Roy S. Sen: On rates of convergence in the central limit theorem for parameter estimation in general autoregressive model. Statistics 21 (1990), 461–470 DOI 10.1080/02331889008802256 | MR 1062852
[3] Bose A.: Edgeworth correction by bootstrap in autoregression. Ann. Statist. 16 (1988), 1709–1722 DOI 10.1214/aos/1176351063 | MR 0964948
[4] Brockwell P. J., Davis R. A.: Time Series: Theory and Methods. Springer–Verlag, New York 1987 MR 0868859 | Zbl 1169.62074
[5] Brown B. M.: Martingale central limit theorems. Ann. Math. Statist. 42 (1971), 59–66 DOI 10.1214/aoms/1177693494 | MR 0290428 | Zbl 0218.60048
[6] Datta S.: On asymptotic properties of bootstrap for AR(1) processes. J. Statist. Plann. Inference 53 (1996), 361–374 DOI 10.1016/0378-3758(95)00147-6 | MR 1407648 | Zbl 0854.62046
[7] Davidson J.: Stochastic Limit Theory. Oxford University Press, New York 1994 MR 1430804 | Zbl 0904.60002
[8] Dürr D., Loges W.: Large deviation results and rates in the central limit theorem for parameter estimators for autoregressive processes. Sankhy$\overline{\mathrm a}$ 47 (1985), Series A, 6–24 MR 0813440 | Zbl 0566.60029
[9] Ferretti N., Romo J.: Unit root bootstrap tests for AR(1) models. Biometrika 83 (1996), 849–860 DOI 10.1093/biomet/83.4.849 | MR 1440049 | Zbl 0883.62099
[10] Hall P., Heyde C. C.: Martingale Limit Theory and Its Application. Academic Press, New York 1980 MR 0624435 | Zbl 0462.60045
[11] Heimann G., Kreiss J. P.: Bootstrapping general first order autoregression. Statist. Probab. Lett. 30 (1996), 87–98 DOI 10.1016/0167-7152(95)00205-7 | MR 1411185 | Zbl 0903.62072
[12] Kreiss J. P., Franke J.: Bootstrapping stationary autoregressive moving-average models. J. Time Ser. Anal. 13 (1992), 297–31 DOI 10.1111/j.1467-9892.1992.tb00109.x | MR 1173561 | Zbl 0787.62092
[13] Kreiss J. P.: Asymptotical properties of residual bootstrap for autoregression. Preprint, TU Braunschweig 1997
[14] Liu R. Y.: Bootstrap procedures under some non-i. i.d. models. Ann. Statist. 16 (1988), 1696–1708 MR 0964947 | Zbl 0655.62031
[15] Michel R., Pfanzagl J.: The accuracy of the normal approximations for minimum contrast estimate. Z. Wahrsch. Verw. Gebiete 18 (1971), 73–84 DOI 10.1007/BF00538488 | MR 0288897
[16] Prášková Z.: A contribution to bootstrapping autoregressive processes. Kybernetika 31 (1995), 359–373 MR 1357350
[17] Prášková Z.: Bootstrapping in autoregression with heteroscedasticities. Working paper, Charles University 1997. Unpublished
[18] Tjøstheim D., Paulsen J.: Least squares estimates and order determination procedures for autoregressive process with a time dependent variance. J. Time Ser. Anal. 6 (1985), 117–138 DOI 10.1111/j.1467-9892.1985.tb00403.x | MR 0797534
[19] Wu C. F. J.: Jackknife, bootstrap and other resampling methods in regression analysis. Ann. Statist. 14 (1986), 1261–1295 DOI 10.1214/aos/1176350142 | MR 0868303 | Zbl 0618.62072
Partner of
EuDML logo