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An extended exponential SEMIFAR model with application in R

Yuanhua Feng (), Jan Beran () and Sebastian Letmathe ()
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Jan Beran: University of Konstanz
Sebastian Letmathe: Paderborn University

No 145, Working Papers CIE from Paderborn University, CIE Center for International Economics

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.

Keywords: long memory; data-driven smoothing; ESEMIFAR; estimation of derivatives (search for similar items in EconPapers)
JEL-codes: C14 C51 (search for similar items in EconPapers)
Pages: 17 pages
Date: 2021-09
New Economics Papers: this item is included in nep-ets and nep-ore
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