In the context of limited dependence at large lags, Andrews (2002) showed the magnitudes of the error in rejection probabilities of the symmetric two-sided block bootstrap t, Wald and J tests. Andrews (2004) introduced the block-block bootstrap and proved that it obtained better asymptotic refinements than the block bootstrap. To date the ability to obtain asymptotic refinements with bootstrap methods has been restricted to data with very limited dependence. In this paper we show that the ability to obtain asymptotic refinements extends to the very important case of AR(∞) models. Specifically, we show that the block-block bootstrap can also provide refinements in the presence of AR(∞) models. We provide the assumptions under which those refinements are possible.
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Paper provided by Iowa State University, Department of Economics in its series Staff General Research Papers with number
12965.
Length: 10 pages Date of creation: 23 Jul 2008 Date of revision: Publication status: Published in Working paper, 2008. Handle: RePEc:isu:genres:12965
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Find related papers by JEL classification: C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General