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Statistical analysis of autoregressive fractionally integrated moving average models in R

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  • Javier Contreras-Reyes
  • Wilfredo Palma

Abstract

The autoregressive fractionally integrated moving average (ARFIMA) processes are one of the best-known classes of long-memory models. In the package afmtools for R, we have implemented a number of statistical tools for analyzing ARFIMA models. In particular, this package contains functions for parameter estimation, exact autocovariance calculation, predictive ability testing and impulse response function computation, among others. Furthermore, the implemented methods are illustrated with applications to real-life time series. Copyright Springer-Verlag Berlin Heidelberg 2013

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  • Javier Contreras-Reyes & Wilfredo Palma, 2013. "Statistical analysis of autoregressive fractionally integrated moving average models in R," Computational Statistics, Springer, vol. 28(5), pages 2309-2331, October.
    • Handle: RePEc:spr:compst:v:28:y:2013:i:5:p:2309-2331
    DOI: 10.1007/s00180-013-0408-7
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    Cited by:

    1. Kanchana Nadarajah & Gael M Martin & Donald S Poskitt, 2019. "Optimal Bias Correction of the Log-periodogram Estimator of the Fractional Parameter: A Jackknife Approach," Monash Econometrics and Business Statistics Working Papers 7/19, Monash University, Department of Econometrics and Business Statistics.
    2. Pushpa Dissanayake & Teresa Flock & Johanna Meier & Philipp Sibbertsen, 2021. "Modelling Short- and Long-Term Dependencies of Clustered High-Threshold Exceedances in Significant Wave Heights," Mathematics, MDPI, vol. 9(21), pages 1-33, November.
    3. Goodness C. Aye & Mehmet Balcilar & Rangan Gupta & Nicholas Kilimani & Amandine Nakumuryango & Siobhan Redford, 2014. "Predicting BRICS stock returns using ARFIMA models," Applied Financial Economics, Taylor & Francis Journals, vol. 24(17), pages 1159-1166, September.
    4. Contreras-Reyes, Javier E., 2022. "Rényi entropy and divergence for VARFIMA processes based on characteristic and impulse response functions," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    5. Byron J. Idrovo-Aguirre & Javier E. Contreras-Reyes, 2019. "Backcasting cement production and characterizing cement’s economic cycles for Chile 1991–2015," Empirical Economics, Springer, vol. 57(5), pages 1829-1852, November.
    6. Byron J. Idrovo-Aguirre & Javier E. Contreras-Reyes, 2021. "The Response of Housing Construction to a Copper Price Shock in Chile (2009–2020)," Economies, MDPI, vol. 9(3), pages 1-11, June.
    7. Hajirahimi, Zahra & Khashei, Mehdi, 2022. "Series Hybridization of Parallel (SHOP) models for time series forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 596(C).
    8. Javier E. Contreras-Reyes & Joaquín E. Zavala & Byron J. Idrovo-Aguirre, 2024. "Analyzing the Selective Stock Price Index Using Fractionally Integrated and Heteroskedastic Models," JRFM, MDPI, vol. 17(9), pages 1-17, September.
    9. Idrovo Aguirre, Byron & Contreras, Javier, 2015. "Back-splicing of cement production and characterization of its economic cycle: The case of Chile (1991-2015)," MPRA Paper 67387, University Library of Munich, Germany, revised 20 Sep 2015.
    10. Diego Chávez & Javier E. Contreras-Reyes & Byron J. Idrovo-Aguirre, 2022. "A Threshold GARCH Model for Chilean Economic Uncertainty," JRFM, MDPI, vol. 16(1), pages 1-15, December.
    11. Contreras-Reyes, Javier E. & Idrovo-Aguirre, Byron J., 2020. "Backcasting and forecasting time series using detrended cross-correlation analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 560(C).

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