Bootstrapping autoregressions with conditional heteroskedasticity of unknown form.
Conditional heteroskedasticity is an important feature of many macroeconomic and financial time series. Standard residual-based bootstrap procedures for dynamic regression models treat the regression error as i.i.d. These procedures are invalid in the presence of conditional heteroskedasticity. We e...
| Publicado en: | Journal of Econometrics Vol. 123; no. 1; pp. 89 - 121 |
|---|---|
| Autores principales: | , |
| Formato: | Artículo |
| Publicado: |
Elsevier Science
November 2004
|
| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=513179123&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 513179123 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 03044076 ECM jtl: Journal of Econometrics issn: 03044076 maglogo: N pubinfo: dt: November 2004 vid: 123 iid: 1 pid: 1004 pub: Elsevier Science artinfo: ui: 513179123 10.1016/j.jeconom.2003.10.030 ppf: 89 ppct: 32 formats: tig: atl: Bootstrapping autoregressions with conditional heteroskedasticity of unknown form. aug: au: Gonçalves, Sílvia Kilian, Lutz su: Autoregression (Statistics) Statistical bootstrapping Variances sug: subj: Autoregression (Statistics) Statistical bootstrapping Variances ab: Conditional heteroskedasticity is an important feature of many macroeconomic and financial time series. Standard residual-based bootstrap procedures for dynamic regression models treat the regression error as i.i.d. These procedures are invalid in the presence of conditional heteroskedasticity. We establish the asymptotic validity of three easy-to-implement alternative bootstrap proposals for stationary autoregressive processes with martingale difference errors subject to possible conditional heteroskedasticity of unknown form. These proposals are the fixed-design wild bootstrap, the recursive-design wild bootstrap and the pairwise bootstrap. In a simulation study all three procedures tend to be more accurate in small samples than the conventional large-sample approximation based on robust standard errors. In contrast, standard residual-based bootstrap methods for models with i.i.d. errors may be very inaccurate if the i.i.d. assumption is violated. We conclude that in many empirical applications the proposed robust bootstrap procedures should routinely replace conventional bootstrap procedures for autoregressions based on the i.i.d. error assumption. Copyright (c) 2003 Elsevier B.V. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
|---|