FitFGN {FGN}R Documentation

MLE estimation for FGN

Description

Exact MLE estimation for FGN

Usage

FitFGN(z, demean = TRUE, MeanMLEQ = FALSE, lag.max = "default")

Arguments

z time series, vector or ts object.
demean if True, subtract mean. Otherwise assume it is zero.
MeanMLEQ if True, an iterative algorithm is used for exact simultaneous MLE estimation of the mean and other parameters.
lag.max the residual autocorrelations are tabulated for lags 1, ..., lag.max. Also lag.max is used for the Ljung-Box portmanteau test.

Details

The exact loglikelihood function is maximized numerically using optimize. The standard error for the H parameter is estimated (McLeod, Yu and Krougly, 2007).

Value

A list with class name "FitAR" and components:

loglikelihood value of the loglikelihood
H estimate of H parameter
SEH SE of H estimate
sigsqHat innovation variance estimate
muHat estimate of the mean
SEmu SE of mean
Rsq R-squared, coefficient of forecastability
LjungBox table of Ljung-Box portmanteau test statistics
res normalized residuals, same length as z
demean TRUE if mean estimated otherwise assumed zero
IterationCount number of iterations in mean mle estimation
MLEMeanQ TRUE if mle for mean algorithm used
tsp tsp(z)
call result from match.call() showing how the function was called
DataTitle returns attr(z,"title")

Author(s)

A.I. McLeod

References

McLeod, A.I., Yu, Hao, Krougly, Zinovi L. (2007). Algorithms for Linear Time Series Analysis, Journal of Statistical Software.

See Also

GetFitFGN, FitRegressionFGN, Boot.FitFGN, coef.FitFGN, plot.FitFGN, print.FitFGN, summary.FitFGN, HurstK

Examples

data(NileMin)
out<-FitFGN(NileMin)
summary(out)
plot(out)
coef(out)

[Package FGN version 1.1 Index]