Stationary Bootstrapping for the Nonparametric AR-ARCH Model
- Authors
- Shin, Dong Wan; Hwang, Eunju
- Issue Date
- Sep-2015
- Publisher
- KOREAN STATISTICAL SOC
- Keywords
- stationary bootstrap; ARCH; nonparametric regression; consistency
- Citation
- COMMUNICATIONS FOR STATISTICAL APPLICATIONS AND METHODS, v.22, no.5, pp.463 - 473
- Journal Title
- COMMUNICATIONS FOR STATISTICAL APPLICATIONS AND METHODS
- Volume
- 22
- Number
- 5
- Start Page
- 463
- End Page
- 473
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/10185
- DOI
- 10.5351/CSAM.2015.22.5.463
- ISSN
- 2287-7843
- Abstract
- We consider a nonparametric AR(1) model with nonparametric ARCH(1) errors. In order to estimate the unknown function of the ARCH part, we apply the stationary bootstrap procedure, which is characterized by geometrically distributed random length of bootstrap blocks and has the advantage of capturing the dependence structure of the original data. The proposed method is composed of four steps: the first step estimates the AR part by a typical kernel smoothing to calculate AR residuals, the second step estimates the ARCH part via the Nadaraya-Watson kernel from the AR residuals to compute ARCH residuals, the third step applies the stationary bootstrap procedure to the ARCH residuals, and the fourth step defines the stationary bootstrapped Nadaraya-Watson estimator for the ARCH function with the stationary bootstrapped residuals. We prove the asymptotic validity of the stationary bootstrap estimator for the unknown ARCH function by showing the same limiting distribution as the Nadaraya-Watson estimator in the second step.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - 사회과학대학 > 응용통계학과 > 1. Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.