Bias Correction of Persistence Measures in Fractionally Integrated Models

Simone D. Grose (), Gael Martin and Donald Poskitt

No 29/13, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

Abstract: This paper investigates the accuracy of bootstrap-based bias correction of persistence measures for long memory fractionally integrated processes. The bootstrap method is based on the semi-parametric sieve approach, with the dynamics in the long memory process captured by an autoregressive approximation. With a view to improving accuracy, the sieve method is also applied to data pre-filtered by a semi-parametric estimate of the long memory parameter. Both versions of the bootstrap technique are used to estimate the finite sample distributions of the sample autocorrelation coefficients and the impulse response coefficients and, in turn, to bias-adjust these statistics. The accuracy of the resultant estimators in the case of the autocorrelation coefficients is also compared with that yielded by analytical bias adjustment methods when available.

Keywords: and phrases:Long memory; ARFIMA; sieve bootstrap; bootstrap-based bias correction; sample autocorrelation function; impulse response function. (search for similar items in EconPapers)
Date: 2013
New Economics Papers: this item is included in nep-ecm and nep-ets
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Related works:
Journal Article: Bias Correction of Persistence Measures in Fractionally Integrated Models (2015)
Working Paper: Bias Correction of Persistence Measures in Fractionally Integrated Models (2014)
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