Prediction Intervals in Linear and Nonlinear Time Series with Sieve Bootstrap Methodology

2014 ◽  
pp. 255-273
Author(s):  
Héctor Allende ◽  
Gustavo Ulloa ◽  
Héctor Allende-Cid
Keyword(s):  
Time Series ◽  
Nonlinear Time Series ◽  
Prediction Intervals ◽  
Sieve Bootstrap ◽  
Linear And Nonlinear ◽  
Bootstrap Methodology

ESTIMATION OF CHANGE-POINTS IN LINEAR AND NONLINEAR TIME SERIES MODELS

2014 ◽  
Vol 32 (2) ◽  
pp. 402-430 ◽  
Author(s):  
Shiqing Ling
Keyword(s):  
Time Series ◽  
Asymptotic Theory ◽  
Nonlinear Time Series ◽  
Change Points ◽  
Time Series Models ◽  
Finite Sample ◽  
Asymptotically Normal ◽  
Estimated Parameters ◽  
Linear And Nonlinear ◽  
Economic Studies

This paper develops an asymptotic theory for estimated change-points in linear and nonlinear time series models. Based on a measurable objective function, it is shown that the estimated change-point converges weakly to the location of the maxima of a double-sided random walk and other estimated parameters are asymptotically normal. When the magnitude d of changed parameters is small, it is shown that the limiting distribution can be approximated by the known distribution as in Yao (1987, Annals of Statistics 15, 1321–1328). This provides a channel to connect our results with those in Picard (1985, Advances in Applied Probability 17, 841–867) and Bai, Lumsdaine, and Stock (1998, Review of Economic Studies 65, 395–432), where the magnitude of changed parameters depends on the sample size n and tends to zero as n → ∞. The theory is applied for the self-weighted QMLE and the local QMLE of change-points in ARMA-GARCH/IGARCH models. A simulation study is carried out to evaluate the performance of these estimators in the finite sample.

scholarly journals Volatility of linear and nonlinear time series

2005 ◽  
Vol 72 (1) ◽  
Author(s):  
Tomer Kalisky ◽  
Yosef Ashkenazy ◽  
Shlomo Havlin
Keyword(s):  
Time Series ◽  
Nonlinear Time Series ◽  
Linear And Nonlinear
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