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  1. Bootstrapping autoregressions with conditional heteroskedasticity of unknown form
    Author: Gonçalves, Sílvia; Kilian, Lutz
    Published: 2002
    Publisher:  Dt. Bundesbank, Frankfurt am Main

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    Freie Universität Berlin, Universitätsbibliothek
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    Source: Philologische Bibliothek, FU Berlin
    Language: English
    Media type: Book
    ISBN: 3935821352
    RVK Categories: QB 910 ; QH 233
    Series: Discussion paper / Economic Research Centre of the Deutsche Bundesbank ; 2002,26
    Scope: 44 S.
  2. Bootstrapping autoregressions with conditional heteroskedasticity of unknown form
    Author: Gonçalves, Sílvia; Kilian, Lutz
    Published: 2002
    Publisher:  Dt. Bundesbank, Press and Public Relations Div., Frankfurt am Main

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    Content information
    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Print
    ISBN: 9783935821353; 3935821352
    RVK Categories: QH 233 ; QB 910 ; QM 430 ; QH 233 ; QB 910
    DDC Categories: 330; 380; 650; 670
    Series: Discussion paper / Volkswirtschaftliches Forschungszentrum der Deutschen Bundesbank ; 02,26
    Subjects: ARCH-Prozess; Stochastischer Prozess; Volatilität; Zeitreihenanalyse; Theorie; Bootstrap-Statistik; Heteroskedastizität
    Other subjects: (stw)ARCH-Modell; (stw)Stochastischer Prozess; (stw)Volatilität; (stw)Zeitreihenanalyse; (stw)Theorie; (stw)Bootstrap-Verfahren; (stw)Heteroskedastizität; Online-Publikation; Arbeitspapier; Arbeitspapier; Graue Literatur
    Scope: 44 S., graph. Darst., 30 cm
    Notes:

    Literaturverz. S. 36 - 38
  3. Bootstrapping autoregressions with conditional heteroskedasticity of unknown form
    Author: Gonçalves, Sílvia; Kilian, Lutz
    Published: 2002
    Publisher:  Dt. Bundesbank, Frankfurt am Main

    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... more

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    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-toimplement alternative bootstrap proposals for stationary autoregressive processes with m.d.s. 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 based on the i.i.d. error assumption.

     
    Export to reference management software   RIS file
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    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Print
    ISBN: 3935821352
    RVK Categories: QH 233 ; QB 910
    Series: Discussion paper / Economic Research Centre of the Deutsche Bundesbank ; 2002,26
    Subjects: ARCH-Modell; Stochastischer Prozess; Volatilität; Zeitreihenanalyse; Theorie; Bootstrap-Verfahren; Heteroskedastizität
    Scope: 44 S, Tab., b
    Notes:

    Literaturverz. S. 36 - 38. - Zsfassungen in dt. und engl. Sprache