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Explicit Equations For Acf In Autoregressive Processes In The Presence Of Heteroscedasticity Disturbances, Samir Safi
Explicit Equations For Acf In Autoregressive Processes In The Presence Of Heteroscedasticity Disturbances, Samir Safi
Journal of Modern Applied Statistical Methods
The autocorrelation function, ACF, is an important guide to the properties of a time series. Explicit equations are derived for ACF in the presence of heteroscedasticity disturbances in pth order autoregressive, AR(p), processes. Two cases are presented: (1) when the disturbance term follows the general covariance matrix, Σ , and (2) when the diagonal elements of Σ are not all identical but σi,j = 0 ∀i ≠ j.
Median-Unbiased Optimal Smoothing And Trend Extraction, Dimitrios D. Thomakos
Median-Unbiased Optimal Smoothing And Trend Extraction, Dimitrios D. Thomakos
Journal of Modern Applied Statistical Methods
The problem of smoothing a time series for extracting its low frequency characteristics, collectively called its trend, is considered. A competitive approach is proposed and compared with existing methods in choosing the optimal degree of smoothing based on the distribution of the residuals from the smooth trend.
Properties Of The Gar(1) Model For Time Series Of Counts, Vasiliki Karioti, Chrys Caroni
Properties Of The Gar(1) Model For Time Series Of Counts, Vasiliki Karioti, Chrys Caroni
Journal of Modern Applied Statistical Methods
Models for time series count data include several proposed by Zeger and Qaqish (1988), subsequently generalized into the GARMA family. The GAR(1) model is examined in detail. The maximum likelihood estimation of the parameters will be discussed and the properties of Pearson and randomized residuals will be examined.
Statistical Methods And Artificial Neural Networks, Mammadagha Mammadov, Berna Yazici, Şenay Yolaçan, Atilla Aslanargun, Ali Fuat YüZer, Embiya Ağaoğlu
Statistical Methods And Artificial Neural Networks, Mammadagha Mammadov, Berna Yazici, Şenay Yolaçan, Atilla Aslanargun, Ali Fuat YüZer, Embiya Ağaoğlu
Journal of Modern Applied Statistical Methods
Artificial Neural Networks and statistical methods are applied on real data sets for forecasting, classification, and clustering problems. Hybrid models for two components are examined on different data sets; tourist arrival forecasting to Turkey, macro-economic problem on rescheduling of the countries’ international debts, and grouping twenty-five European Union member and four candidate countries according to macro-economic indicators.
Jmasm10: A Fortran Routine For Sieve Bootstrap Prediction Intervals, Andrés M. Alonso
Jmasm10: A Fortran Routine For Sieve Bootstrap Prediction Intervals, Andrés M. Alonso
Journal of Modern Applied Statistical Methods
A Fortran routine for constructing nonparametric prediction intervals for a general class of linear processes is described. The approach uses the sieve bootstrap procedure of Bühlmann (1997) based on residual resampling from an autoregressive approximation to the given process.
A Recursive Algorithm For Fractionally Differencing Long Data Series, Joseph Mccarthy, Robert Disario, Hakan Saraoglu
A Recursive Algorithm For Fractionally Differencing Long Data Series, Joseph Mccarthy, Robert Disario, Hakan Saraoglu
Journal of Modern Applied Statistical Methods
We propose a recursive algorithm to fractionally difference time series data. The algorithm eliminates the need to evaluate the gamma function directly, and hence avoids the overflow problem that arises when fractionally differencing a long data series. The proposed algorithm can be implemented using any general matrix programming language. An implementation using SAS is presented. The algorithm and the code provide a practical approach to including fractional differencing as part of a time series data analysis.