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Finite Sample Comparison of Parametric, Semiparametric, and Wavelet Estimators of Fractional Integration

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  • Morten �rregaard Nielsen
  • Per Houmann Frederiksen

Abstract

In this paper we compare through Monte Carlo simulations the finite sample properties of estimators of the fractional differencing parameter, d. This involves frequency domain, time domain, and wavelet based approaches, and we consider both parametric and semiparametric estimation methods. The estimators are briefly introduced and compared, and the criteria adopted for measuring finite sample performance are bias and root mean squared error. Most importantly, the simulations reveal that (1) the frequency domain maximum likelihood procedure is superior to the time domain parametric methods, (2) all the estimators are fairly robust to conditionally heteroscedastic errors, (3) the local polynomial Whittle and bias-reduced log-periodogram regression estimators are shown to be more robust to short-run dynamics than other semiparametric (frequency domain and wavelet) estimators and in some cases even outperform the time domain parametric methods, and (4) without sufficient trimming of scales the wavelet-based estimators are heavily biased.

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Bibliographic Info

Article provided by Taylor & Francis Journals in its journal Econometric Reviews.

Volume (Year): 24 (2005)
Issue (Month): 4 ()
Pages: 405-443

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Handle: RePEc:taf:emetrv:v:24:y:2005:i:4:p:405-443

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Related research

Keywords: Bias; Finite sample distribution; Fractional integration; Maximum likelihood; Monte Carlo simulation; Parametric estimation; Semiparametric estimation; Wavelet;

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Citations

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Cited by:
  1. Nuno Cassola & Claudio Morana, 2007. "Comovements in Volatility in the Euro Money Market," ICER Working Papers 7-2007, ICER - International Centre for Economic Research.
  2. Grassi, Stefano & Santucci de Magistris, Paolo, 2014. "When long memory meets the Kalman filter: A comparative study," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 301-319.
  3. D.S. Poskitt & Gael M. Martin & Simone D. Grose, 2014. "Bias Reduction of Long Memory Parameter Estimators via the Pre-filtered Sieve Bootstrap," Monash Econometrics and Business Statistics Working Papers 10/14, Monash University, Department of Econometrics and Business Statistics.
  4. Frank S. Nielsen, 2008. "Local polynomial Whittle estimation covering non-stationary fractional processes," CREATES Research Papers 2008-28, School of Economics and Management, University of Aarhus.
  5. Afonso Gonçalves da Silva & Peter M Robinson, 2006. "Finite Sample Performance in CointegrationAnalysis of Nonlinear Time Series with LongMemory," STICERD - Econometrics Paper Series /2006/501, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
  6. Marcel Aloy & Gilles Dufrenot & Charles Lai Tong & Anne Peguin-Feissolle, 2012. "A Smooth Transition Long-Memory Model," Working Papers halshs-00793680, HAL.
  7. D.S. Poskitt & Simone D. Grose & Gael M. Martin, 2013. "Higher-Order Improvements of the Sieve Bootstrap for Fractionally Integrated Processes," Monash Econometrics and Business Statistics Working Papers 25/13, Monash University, Department of Econometrics and Business Statistics.
  8. Hiremath, Gourishankar S & Bandi, Kamaiah, 2011. "Testing Long Memory in Stock Returns of Emerging Markets: Some Further Evidence," MPRA Paper 48517, University Library of Munich, Germany.
  9. Morana, Claudio, 2006. "A small scale macroeconometric model for the Euro-12 area," Economic Modelling, Elsevier, vol. 23(3), pages 391-426, May.
  10. Marcel Aloy & Gilles de Truchis, 2013. "Optimal Estimation Strategies for Bivariate Fractional Cointegration Systems," AMSE Working Papers 1353, Aix-Marseille School of Economics, Marseille, France, revised 29 Oct 2013.
  11. Aaron Smallwood; Alex Maynard; Mark Wohar, 2005. "The Long and the Short of It: Long Memory Regressors and Predictive Regressions," Computing in Economics and Finance 2005 384, Society for Computational Economics.
  12. Claudio Morana, 2014. "Factor Vector Autoregressive Estimation of Heteroskedastic Persistent and Non Persistent Processes Subject to Structural Breaks," Working Papers 273, University of Milano-Bicocca, Department of Economics, revised May 2014.

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