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Comparing the Bias and Misspecification in ARFIMA Models

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  • Smith, Jeremy
  • Taylor, Nick
  • Yadav, Sanjay

Abstract

We investigate the bias in both the short‐term and long‐term parameters for a range of autoregressive fractional integrated moving‐average (ARFIMA) models using both semi‐parametric and maximum likelihood (ML) estimation methods. The results suggest that, provided the correct model is estimated, the ML method outperforms the semi‐parametric methods in terms of the bias and smaller mean square errors in both the long‐term and short‐term parameter estimates. These biases often cause model selection criteria to select an incorrect ARFIMA specification. Taking account of the potential misspecification the biases associated with the ML procedure tend to increase, although it continues to have a smaller worst‐case bias than either of the semi‐parametric procedures.
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Suggested Citation

  • Smith, Jeremy & Taylor, Nick & Yadav, Sanjay, 1995. "Comparing the Bias and Misspecification in ARFIMA Models," Economic Research Papers 268691, University of Warwick - Department of Economics.
    • Handle: RePEc:ags:uwarer:268691
    DOI: 10.22004/ag.econ.268691
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    Cited by:

    1. Pong, Shiuyan & Shackleton, Mark B. & Taylor, Stephen J. & Xu, Xinzhong, 2004. "Forecasting currency volatility: A comparison of implied volatilities and AR(FI)MA models," Journal of Banking & Finance, Elsevier, vol. 28(10), pages 2541-2563, October.
    2. Miguel Arranz & Francesc Marmol, 2001. "Out-of-sample forecast errors in misspecific perturbed long memory processes," Statistical Papers, Springer, vol. 42(4), pages 423-436, October.
    3. Giorgio Canarella & Stephen M Miller, 2017. "Inflation Persistence Before and After Inflation Targeting: A Fractional Integration Approach," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 43(1), pages 78-103, January.
    4. Leonardo Rocha Souza, 2007. "Temporal Aggregation and Bandwidth selection in estimating long memory," Journal of Time Series Analysis, Wiley Blackwell, vol. 28(5), pages 701-722, September.
    5. Ana Pérez & Esther Ruiz, 2002. "Modelos de memoria larga para series económicas y financieras," Investigaciones Economicas, Fundación SEPI, vol. 26(3), pages 395-445, September.
    6. Fantazzini, Dean, 2023. "Assessing the Credit Risk of Crypto-Assets Using Daily Range Volatility Models," MPRA Paper 117141, University Library of Munich, Germany.
    7. Gadea, Maria Dolores & Sabate, Marcela & Serrano, Jose Maria, 2004. "Structural breaks and their trace in the memory: Inflation rate series in the long-run," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 14(2), pages 117-134, April.
    8. Goddard, John & Onali, Enrico, 2012. "Short and long memory in stock returns data," Economics Letters, Elsevier, vol. 117(1), pages 253-255.
    9. Giorgio Canarella & Stephen M. Miller, 2016. "Inflation Persistence and Structural Breaks: The Experience of Inflation Targeting Countries and the US," Working papers 2016-11, University of Connecticut, Department of Economics.
    10. Rea, William & Oxley, Les & Reale, Marco & Brown, Jennifer, 2013. "Not all estimators are born equal: The empirical properties of some estimators of long memory," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 93(C), pages 29-42.
    11. Onali, Enrico & Goddard, John, 2011. "Are European equity markets efficient? New evidence from fractal analysis," International Review of Financial Analysis, Elsevier, vol. 20(2), pages 59-67, April.
    12. Leonardo Souza & Jeremy Smith & Reinaldo Souza, 2006. "Convex combinations of long memory estimates from different sampling rates," Computational Statistics, Springer, vol. 21(3), pages 399-413, December.
    13. Guglielmo Caporale & Luis Gil-Alana, 2013. "Long memory in US real output per capita," Empirical Economics, Springer, vol. 44(2), pages 591-611, April.
    14. Souza, Leonardo R. & Smith, Jeremy, 2004. "Effects of temporal aggregation on estimates and forecasts of fractionally integrated processes: a Monte-Carlo study," International Journal of Forecasting, Elsevier, vol. 20(3), pages 487-502.
    15. Benjamin J. C. Kim & David Karemera, 2006. "Assessing the forecasting accuracy of alternative nominal exchange rate models: the case of long memory," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(5), pages 369-380.
    16. Shuping Shi & Jun Yu, 2023. "Volatility Puzzle: Long Memory or Antipersistency," Management Science, INFORMS, vol. 69(7), pages 3861-3883, July.
    17. Silva, E.M. & Franco, G.C. & Reisen, V.A. & Cruz, F.R.B., 2006. "Local bootstrap approaches for fractional differential parameter estimation in ARFIMA models," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 1002-1011, November.
    18. Beran, Jan & Ghosh, Sucharita & Schell, Dieter, 2009. "On least squares estimation for long-memory lattice processes," Journal of Multivariate Analysis, Elsevier, vol. 100(10), pages 2178-2194, November.
    19. Steven Clark & T. Coggin, 2011. "Are U.S. stock prices mean reverting? Some new tests using fractional integration models with overlapping data and structural breaks," Empirical Economics, Springer, vol. 40(2), pages 373-391, April.

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