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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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    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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