Description Usage Arguments Value Note References See Also Examples
Fits an ARFIMA(p,d,q) model to a time series using a minimum distance estimator. For details see Mayoral (2007).
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y |
Numeric vector of the time series. |
p |
Autoregressive order. |
q |
Moving average order. |
d.range |
Range of allowable values for fractional differencing parameter. Smallest value must be greater than -1. |
start |
Named vector of length 1 + |
lag.max |
Maximum lag to use when calculating the residual autocorrelations. For details see Mayoral (2007). |
incl.mean |
Whether or not to include a mean term in the model. The default value of |
verbose |
Whether to print summary of fit. |
method |
Method for |
control |
List of additional arguments for |
A list containing:
pars | A numeric vector of parameter estimates. |
std.errs | A numeric vector of standard errors on parameters. |
cov.mat | Parameter covariance matrix (excluding mean). |
fit.obj | optim fit object. |
p.val | Ljung-Box p-value for fit. |
residuals | Fit residuals. |
This method makes no assumptions on the distribution of the innovation series, and the innovation variance does not factor into the covariance matrix of parameter estimates. For this reason, it is not included in the results, but can be estimated from the residuals—see Mayoral (2007).
Mayoral, L. (2007). Minimum distance estimation of stationary and non-stationary ARFIMA processes. The Econometrics Journal, 10, 124-148. doi: 10.1111/j.1368-423X.2007.00202.x
mle.arfima
for psuedo-maximum likelihood estimation.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | set.seed(1)
x <- arfima.sim(1000, d=0.6, ar=c(-0.4))
fit <- mde.arfima(x, p=1, incl.mean=FALSE, verbose=TRUE)
## Fit Summary
## --------------------
## Ljung-Box p-val: 0.276
## d ar.1
## est 0.55031 -0.39261
## err 0.03145 0.03673
##
## Correlation Matrix for ARFIMA Parameters
## d ar.1
## d 1.0000 0.6108
## ar.1 0.6108 1.0000
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