Smoothing Long-Memory Time Series
esemifar: A package for data-driven nonparametric estimation of the tr...
Data-driven Local Polynomial for the Trend's Derivatives in Equidistan...
AR Representation of an ARMA Model
MA Representation of an ARMA Model
FARIMA Order Selection Matrix
Filter Coefficients of the Fractional Differencing Operator
AR Representation of a FARIMA Model
MA Representation of a FARIMA Model
Extract Model Fitted Values
Estimation of Trends and their Derivatives via Local Polynomial Regres...
Plot Method for the Package 'esemifar'
Plot Method for Class "esemifar_fc"
ESEMIFAR Prediction Method
Print Method for the Package 'esemifar'
Extract Model Residuals
Advanced Data-driven Nonparametric Regression for the Trend in Equidis...
The nonparametric trend and its derivatives in equidistant time series (TS) with long-memory errors can be estimated. The estimation is conducted via local polynomial regression using an automatically selected bandwidth obtained by a built-in iterative plug-in algorithm or a bandwidth fixed by the user. The smoothing methods of the package are described in Letmathe, S., Beran, J. and Feng, Y., (2023) <doi:10.1080/03610926.2023.2276049>.