Zhu, Ke and Yu, Philip L.H. and Li, Wai Keung (2013): Testing for the buffered autoregressive processes.
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Abstract
This paper investigates a quasi-likelihood ratio (LR) test for the thresholds in buffered autoregressive processes. Under the null hypothesis of no threshold, the LR test statistic converges to a function of a centered Gaussian process. Under local alternatives, this LR test has nontrivial asymptotic power. Furthermore, a bootstrap method is proposed to obtain the critical value for our LR test. Simulation studies and one real example are given to assess the performance of this LR test. The proof in this paper is not standard and can be used in other non-linear time series models.
Item Type: | MPRA Paper |
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Original Title: | Testing for the buffered autoregressive processes |
English Title: | Testing for the buffered autoregressive processes |
Language: | English |
Keywords: | AR(p) model; Bootstrap method; Buffered AR(p) model; Likelihood ratio test; Marked empirical process; Threshold AR(p) model. |
Subjects: | C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C12 - Hypothesis Testing: General |
Item ID: | 51706 |
Depositing User: | Dr. Ke Zhu |
Date Deposited: | 26 Nov 2013 07:34 |
Last Modified: | 30 Sep 2019 21:25 |
References: | [1] Andrews, D.W.K. (1993a) Tests for parameter instability and structural change with unknow change point. Econometrica 61, 821-856. [2] Andrews, D.W.K. (1993b) An introduction to econometric applications of functional limit theory for dependent random variables. Econometric Reviews 12, 183-216. [3] Caner, M. and Hansen, B.E. (2001) Threshold autoregression with a unit root. Econometrica 69, 1555-1596. [4] Chan, K.S. (1990) Testing for threshold autoregression. Annals of Statistics 18, 1886-1894. [5] Chan, K.S. (1991) Percentage points of likelihood ratio tests for threshold autoregression. Journal of the Royal Statistical Society Series B 53, 691-696. [6] Chan, K.S. and Tong, H. (1990) On likelihood ratio tests for threshold autoregression. Journal of the Royal Statistical Society Series B 52, 469-476. [7] Davies, R.B. (1977) Hypothesis testing when a nuisance parameter is present only under the alternative. Biometrica 64, 247-254. [8] Davies, R.B. (1987) Hypothesis testing when a nuisance parameter is present only under the alternative. Biometrica 74, 33-43. [9] Doukhan, P., Massart, P., and Rio, E. (1995) Invariance principles for absolutely regular empirical processes. Annales de I'Institut H. Poincare 31 393-427. [10] Hansen, B.E. (1996) Inference when a nuisance parameter is not indenti¯ed under the null hypothesis. Econometrica 64, 413-430. [11] Hansen, B.E. (1999) Threshold effects in non-dynamic panels: Estimation, testing, and inference. Journal of Econometrics 93, 345-368. [12] Li, G.D., Guan, B., Li, W.K., and Yu, P.L.H. (2012) Buffered threshold autoregressive time series models. Working paper. University of Hong Kong. [13] Li, G.D. and Li, W.K. (2008) Testing for threshold moving average with conditional heteroscedasticity. Statistica Sinica 18, 647-665. [14] Li, G.D. and Li, W.K. (2011) Testing a linear time series models against its threshold extension. Biometrika 98, 243-250. [15] Li, D. and Ling, S. (2013) On a threshold double autoregressive model. Working paper. Hong Kong University of Science and Technology. [16] Ling, S. and Tong, H. (2005) Testing a linear MA model against threshold MA models. Annals of Statistics 33, 2529-2552. [17] Pham, T.D. and Tran, L.T. (1985) Some mixing properties of time series models. Stochastic Processes and their applications 19, 297-303. [18] Potter, S.M. (1995) A nonlinear approach to U.S. GNP. Journal of Applied Econometrics 10, 109-125. [19] Stute, W. (1997) Nonparametric model checks for regression. Annals of Statistics 25, 613-641. [20] Tiao, G.C. and Tsay, R.S. (1994) Some advances in non-linear and adaptive modelling in time-series. Journal of Forecasting 13, 109-131. [21] Tong, H. (1978) On a threshold model. In Pattern Recognition and Signal Processing (C.H. Chen, ed.) 101-141. Sijthoff and Noordhoff, Amsterdam. [22] Tong, H. (1990) Non-linear Time Series. A Dynamical System Approach. Clarendon Press, Oxford. [23] Tong, H. (2011) Threshold models in time series analysis{30 years on (with discussions). Statistics and Its Interface 4, 107-135. [24] Tsay, R.S. (1989) Testing and modeling threshold autoregressive processes. Journal of American Statistical Association 84, 231-240. [25] Tsay, R.S. (1998) Testing and modeling multivariate threshold models. Journal of American Statistical Association 93, 1188-1202. [26] Wong, C.S. and Li, W.K. (1997) Testing for threshold autoregression with conditional heteroscedasticity. Biometrika 84, 407-418. [27] Wong, C.S. and Li, W.K. (2000) Testing for double threshold autoregressive conditional heteroscedastic model. Statistica Sinica 10, 173-189. [28] Zhu, K. and Ling, S. (2012) Likelihood ratio tests for the structural change of an AR(p) model to a threshold AR(p) model. Journal of Time Series Analysis 33, 223-232. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/51706 |