Sizes of two bootstrap-based nonparametric specification tests for the drift function in continuous time models

被引:5
|
作者
Kim, MS [1 ]
Wang, SJ [1 ]
机构
[1] Texas A&M Univ, Dept Stat, College Stn, TX 77843 USA
关键词
bandwidth; drift function; generalized likelihood ratio test; nonparametric model specification test; wild bootstrap;
D O I
10.1016/j.csda.2005.02.009
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
We investigate the sizes of the bootstrap versions of the generalized likelihood ratio (GLR) test by Fan et al. [2001. Ann. Statist. 29, 153-193] and of the nonparametric model specification test by Li [1994. Discussion Paper No. 1994-1997, University of Guelph] and Zheng [1996. J. Econometrics 75, 263-289], henceforth the J(n) test for the drift function in some continuous time models. Fan and Zhang [2003. J. Amer. Statist. Assoc. 98, 118-134] argued that the bootstrap-based GLR test is a powerful testing method for model specification of several one-factor continuous time models. However, if the sizes of nonparametric specification tests are unstable over a range of bandwidth values, it is difficult to judge the power of the test. Our simulation study shows that in some standard finite sample situations the bootstrap-based GLR test does not provide stable sizes over a grid of bandwidth values in testing the drift function of some continuous time models, whereas such J, test usually does. Furthermore, we consider the wild bootstrap-based GLR test, inspired by the wild bootstrap approach used for the J, test. The conclusion is that the modified method does not show much improvement on the stable sizes. (C) 2005 Elsevier B.V. All rights reserved.
引用
收藏
页码:1793 / 1806
页数:14
相关论文
共 31 条
  • [21] CONVERGENCE RATES OF SUMS OF α-MIXING TRIANGULAR ARRAYS: WITH AN APPLICATION TO NONPARAMETRIC DRIFT FUNCTION ESTIMATION OF CONTINUOUS-TIME PROCESSES
    Kanaya, Shin
    ECONOMETRIC THEORY, 2017, 33 (05) : 1121 - 1153
  • [22] Nonparametric tests of independence of two autoregressive time series based on autoregression rank scores
    Hallin, M
    Jurecková, J
    Picek, J
    Zahaf, T
    JOURNAL OF STATISTICAL PLANNING AND INFERENCE, 1999, 75 (02) : 319 - 330
  • [23] Nonparametric inference for continuous-time event counting and link-based dynamic network models
    Kreiss, Alexander
    Mammen, Enno
    Polonik, Wolfgang
    ELECTRONIC JOURNAL OF STATISTICS, 2019, 13 (02): : 2764 - 2829
  • [24] Moment based regression algorithms for drift and volatility estimation in continuous-time Markov switching models
    Elliott, Robert J.
    Krishnamurthy, Vikram
    Sass, Joern
    ECONOMETRICS JOURNAL, 2008, 11 (02): : 244 - 270
  • [25] Likelihood-based specification analysis of continuous-time models of the short-term interest rate
    Durham, GB
    JOURNAL OF FINANCIAL ECONOMICS, 2003, 70 (03) : 463 - 487
  • [26] COMBINING BOOTSTRAP-BASED STROKE INCIDENCE MODELS WITH DISCRETE EVENT MODELING OF TRAVEL-TIME AND STROKE TREATMENT: NON-NORMAL INPUT AND NON-LINEAR OUTPUT
    Rand-Hendriksen, Kim
    Viana, Joe
    Dahl, Fredrik
    2017 WINTER SIMULATION CONFERENCE (WSC), 2017, : 1670 - 1679
  • [27] UNIFORM CONVERGENCE RATES OF KERNEL-BASED NONPARAMETRIC ESTIMATORS FOR CONTINUOUS TIME DIFFUSION PROCESSES: A DAMPING FUNCTION APPROACH
    Kanaya, Shin
    ECONOMETRIC THEORY, 2017, 33 (04) : 874 - 914
  • [28] Tests of two time-dependent seismicity models based on interevent times of mainshocks and on seismic triggering in the Aegean area
    Papazachos, B. C.
    Karakaisis, G. F.
    Papazachos, C. B.
    Scordilis, E. M.
    BOLLETTINO DI GEOFISICA TEORICA ED APPLICATA, 2011, 52 (01) : 39 - 57
  • [29] Adaptive robust control of the continuous-time two-input systems with unknown disturbance based on Q-function
    Lv, Yongfeng
    Cui, Zhengyu
    Wang, Minlin
    2023 IEEE 12TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS CONFERENCE, DDCLS, 2023, : 852 - 856
  • [30] Adaptive robust control of the continuous-Time two-input systems with unknown disturbance based on Q-function
    Lv, Yongfeng
    Cui, Zhengyu
    Wang, Minlin
    Proceedings of 2023 IEEE 12th Data Driven Control and Learning Systems Conference, DDCLS 2023, 2023, : 852 - 856