The main objective of this paper is to establish the residual and the wild bootstrap procedures for periodically autoregressive models. We use the least squares estimators of model’s parameters and generate their bootstrap equivalents. We prove that the bootstrap procedures for causal periodic autoregressive time series with finite fourth moments are weakly consistent. Finally, we confirm our theoretical considerations by simulations.
Accepté le :
DOI : 10.1051/ps/2017017
Mots clés : Bootstrap, least squares estimation, periodically autoregressive models, time series
@article{PS_2017__21__394_0, author = {Cio{\l}ek, Gabriela and Potorski, Pawe{\l}}, title = {Bootstrapping periodically autoregressive models}, journal = {ESAIM: Probability and Statistics}, pages = {394--411}, publisher = {EDP-Sciences}, volume = {21}, year = {2017}, doi = {10.1051/ps/2017017}, mrnumber = {3743920}, zbl = {1450.62110}, language = {en}, url = {http://archive.numdam.org/articles/10.1051/ps/2017017/} }
TY - JOUR AU - Ciołek, Gabriela AU - Potorski, Paweł TI - Bootstrapping periodically autoregressive models JO - ESAIM: Probability and Statistics PY - 2017 SP - 394 EP - 411 VL - 21 PB - EDP-Sciences UR - http://archive.numdam.org/articles/10.1051/ps/2017017/ DO - 10.1051/ps/2017017 LA - en ID - PS_2017__21__394_0 ER -
Ciołek, Gabriela; Potorski, Paweł. Bootstrapping periodically autoregressive models. ESAIM: Probability and Statistics, Tome 21 (2017), pp. 394-411. doi : 10.1051/ps/2017017. http://archive.numdam.org/articles/10.1051/ps/2017017/
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