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An extended exponential SEMIFAR model with application in R

Author

Listed:
  • Yuanhua Feng

    (Paderborn University)

  • Jan Beran

    (University of Konstanz)

  • Sebastian Letmathe

    (Paderborn University)

Abstract

The paper at hand provides a detailed description of the esemifar R-package, which is an extension of the already published smoots package, enabling the data-driven local-polynomial smoothing of time series with long-memory. In this regard a sim- ple data-driven algorithm is proposed based on the well-known iterative plug in algorithm for SEMIFAR (semiparametric fractional autoregressive) models. Two new functions for data-driven estimation of the trend and its derivatives under the presence of long-memory are introduced. esemifar is applied to various environ- mental and financial time series with long memory, e.g. mean monthly Northern Hemisphere changes, daily observations of the air quality index of London (Britain), quarterly G7-GDP and daily trading volume of the S&P500. It is worth mentioning that this package can be applied to any suitable time series with long memory.

Suggested Citation

  • Yuanhua Feng & Jan Beran & Sebastian Letmathe, 2021. "An extended exponential SEMIFAR model with application in R," Working Papers CIE 145, Paderborn University, CIE Center for International Economics.
    • Handle: RePEc:pdn:ciepap:145
    as

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    File URL: http://groups.uni-paderborn.de/wp-wiwi/RePEc/pdf/ciepap/WP145.pdf
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    References listed on IDEAS

    as
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    3. repec:adr:anecst:y:2000:i:60:p:05 is not listed on IDEAS
    4. Engle, Robert, 2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 339-350, July.
    5. Andersen, Torben G. & Bollerslev, Tim & Lange, Steve, 1999. "Forecasting financial market volatility: Sample frequency vis-a-vis forecast horizon," Journal of Empirical Finance, Elsevier, vol. 6(5), pages 457-477, December.
    6. Baillie, Richard T. & Chung, Sang-Kuck, 2002. "Modeling and forecasting from trend-stationary long memory models with applications to climatology," International Journal of Forecasting, Elsevier, vol. 18(2), pages 215-226.
    7. C. W. J. Granger & Roselyne Joyeux, 1980. "An Introduction To Long‐Memory Time Series Models And Fractional Differencing," Journal of Time Series Analysis, Wiley Blackwell, vol. 1(1), pages 15-29, January.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    long memory; data-driven smoothing; ESEMIFAR; estimation of derivatives;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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