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Bootstrapping Autoregressions with Conditional Heteroskedasticity of Unknown Form

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  • Kilian, Lutz
  • Gonçalves, Sílvia

Abstract

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. -- Bedingte Heteroskedastizität ist eine wichtige Eigenschaft von vielen Daten über Finanzmärkte und die Makroökonomie. Standard bootstrap Verfahren für dynamische Regressionsmodelle behandeln die Residuen der Regression als i. i. d. Bei bedingter Heteroskedastizität sind diese Prozeduren nicht angemessen. Wir zeigen die asymptotische Gültigkeit von 3 alternativen bootstrap Methoden für stationäre autoregressive Prozesse mit m. d. s. Fehler, die eine bedingte Heteroskedastizität unbekannter Form aufweisen. Es geht dabei um ein fixed-design wild bootstrap, den recursive-design wild bootstrap und den paarweisen bootstrap. In einer Simulationsstudie erscheinen alle 3 Prozeduren in kleinen Stichproben angewandt genauer als die konventionellen Approximationen, die auf robusten Standardfehlern basieren. Diese letztgenannten Methoden können dagegen sehr ungenau sein, wenn die i. i. d. Annahme nicht gilt. Wir schließen daraus, dass bei vielen empirischen Anwendungen die robusten bootstrap Verfahren, die hier vorgestellt werden und leicht zu implementieren sind, die üblichen bootstrap Verfahren ersetzen sollten.

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Bibliographic Info

Paper provided by Deutsche Bundesbank, Research Centre in its series Discussion Paper Series 1: Economic Studies with number 2002,26.

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Date of creation: 2002
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Handle: RePEc:zbw:bubdp1:4191

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Keywords: wild bootstrap; pairwise bootstrap; robust inference;

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