Language Reference |
FARMAFIT Call |
The FARMAFIT subroutine estimates the parameters of an ARFIMA() model.
The input arguments to the FARMAFIT subroutine are as follows:
specifies a time series (assuming mean zero).
specifies the set or subset of the AR order. If you do not specify p, the default is p.
If you specify p=3, the FARMAFIT subroutine estimates the coefficient of the lagged variable .
If you specify p=, the FARMAFIT subroutine estimates the coefficients of lagged variables , , and .
specifies the subset of the MA order. If you do not specify q, the default value is 0.
If you specify q=2, the FARMAFIT subroutine estimates the coefficient of the lagged variable .
If you specify q=, the FARMAFIT subroutine estimates the coefficients of lagged variables and .
specifies the method of computing the log-likelihood function.
requests the conditional sum of squares function. This is the default.
requests the exact log-likelihood function. This option requires that the time series be stationary and invertible.
The FARMAFIT subroutine returns the following values:
is a scalar that contains a fractional differencing order.
is a vector that contains the autoregressive coefficients.
is a vector that contains the moving average coefficients.
is a scalar that contains a variance of the innovation series.
As an example, consider the following ARFIMA() model:
In this model, . The following statements estimate the parameters of this model:
d = 0.3; phi = 0.5; theta = -0.1; call farmasim(yt, d, phi, theta); call farmafit(d, ar, ma, sigma, yt) p=1 q=1; print d ar ma sigma;
The FARMAFIT subroutine estimates the parameters , , , and of an ARFIMA() model. The log-likelihood function is solved by iterative numerical procedures such as the quasi-Newton optimization. The starting value is obtained by the approach of Geweke and Porter-Hudak (1983); the starting values of the AR and MA parameters are obtained from the least squares estimates.
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