Language Reference

FARMAFIT Call

CALL FARMAFIT (d, phi, theta, sigma, series <*>, p <*>, q <*>, opt );

The FARMAFIT subroutine estimates the parameters of an ARFIMA($p,d,q$) model.

The input arguments to the FARMAFIT subroutine are as follows:

series

specifies a time series (assuming mean zero).

p

specifies the set or subset of the AR order. If you do not specify p, the default is p=0.

If you specify p=3, the FARMAFIT subroutine estimates the coefficient of the lagged variable $y_{t-3}$.

If you specify p=$\{ 1,2,3\} $, the FARMAFIT subroutine estimates the coefficients of lagged variables $y_{t-1}$, $y_{t-2}$, and $y_{t-3}$.

q

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 ${\epsilon }_{t-2}$.

If you specify q=$\{ 1,2\} $, the FARMAFIT subroutine estimates the coefficients of lagged variables ${\epsilon }_{t-1}$ and ${\epsilon }_{t-2}$.

opt

specifies the method of computing the log-likelihood function.

0

requests the conditional sum of squares function. This is the default.

1

requests the exact log-likelihood function. This option requires that the time series be stationary and invertible.

The FARMAFIT subroutine returns the following values:

d

is a scalar that contains a fractional differencing order.

phi

is a vector that contains the autoregressive coefficients.

theta

is a vector that contains the moving average coefficients.

sigma

is a scalar that contains a variance of the innovation series.

As an example, consider the following ARFIMA($1,0.3,1$) model:

\[ (1-0.5B)(1-B)^{0.3}y_ t = (1+0.1B){\epsilon }_ t \]

In this model, ${\epsilon }_ t \sim NID(0,1)$. The following statements estimate the parameters of this model: 

d = 0.3;
phi = 0.5;
theta = -0.1;
call farmasim(yt, d, phi, theta) seed=1234;
call farmafit(d, ar, ma, sigma, yt) p=1 q=1;
print d ar ma sigma;

Figure 25.131: Parameter Estimates for a ARFIMA Model

d ar ma sigma
0.3950157 0.5676217 -0.012339 1.2992989 




            

            


            
The FARMAFIT subroutine estimates the parameters d, $\phi (B)$, $\theta (B)$, and $\sigma _{\epsilon }^2$ of an ARFIMA($p,d,q$) model. The log-likelihood function is solved by iterative numerical procedures such as the quasi-Newton optimization. The
               starting value d 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.