g05pgf generates a given number of terms of an exponential
process (see
Engle and Ng (1993)).
An exponential
process is represented by:
where
,
denotes the expected value of
, and
or
. Here
is a standardized Student's
-distribution with
degrees of freedom and variance
,
is the number of observations in the sequence,
is the observed value of the
process at time
,
is the conditional variance at time
, and
the set of all information up to time
.
One of the initialization routines
g05kff (for a repeatable sequence if computed sequentially) or
g05kgf (for a non-repeatable sequence) must be called prior to the first call to
g05pgf.
Engle R (1982) Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom inflation Econometrica 50 987–1008
Glosten L, Jagannathan R and Runkle D (1993) Relationship between the expected value and the volatility of nominal excess return on stocks Journal of Finance 48 1779–1801
- 1: – Character(1)Input
-
On entry: the type of distribution to use for
.
- A Normal distribution is used.
- A Student's -distribution is used.
Constraint:
or .
- 2: – IntegerInput
-
On entry: , the number of terms in the sequence.
Constraint:
.
- 3: – IntegerInput
-
On entry: the number of coefficients,
, for .
Constraint:
.
- 4: – IntegerInput
-
On entry: the number of coefficients,
, for .
Constraint:
.
- 5: – Real (Kind=nag_wp) arrayInput
-
On entry: the initial parameter estimates for the vector
. The first element must contain the coefficient
and the next
iq elements must contain the autoregressive coefficients
, for
. The next
iq elements must contain the coefficients
, for
. The next
ip elements must contain the moving average coefficients
, for
.
Constraints:
- ;
- .
- 6: – IntegerInput
-
On entry: the number of degrees of freedom for the Student's
-distribution.
If
,
df is not referenced.
Constraint:
if , .
- 7: – Real (Kind=nag_wp) arrayOutput
-
On exit: the conditional variances
, for , for the sequence.
- 8: – Real (Kind=nag_wp) arrayOutput
-
On exit: the observations
, for , for the sequence.
- 9: – LogicalInput
-
On entry: if
, a new sequence is to be generated, otherwise a given sequence is to be continued using the information in
r.
- 10: – Real (Kind=nag_wp) arrayInput/Output
-
On entry: the array contains information required to continue a sequence if .
On exit: contains information that can be used in a subsequent call of g05pgf, with .
- 11: – IntegerInput
-
On entry: the dimension of the array
r as declared in the (sub)program from which
g05pgf is called.
Constraint:
.
- 12: – Integer arrayCommunication Array
Note: the actual argument supplied
must be the array
state supplied to the initialization routines
g05kff or
g05kgf.
On entry: contains information on the selected base generator and its current state.
On exit: contains updated information on the state of the generator.
- 13: – IntegerInput/Output
-
On entry:
ifail must be set to
,
. If you are unfamiliar with this argument you should refer to
Section 3.4 in How to Use the NAG Library and its Documentation for details.
For environments where it might be inappropriate to halt program execution when an error is detected, the value
is recommended. If the output of error messages is undesirable, then the value
is recommended. Otherwise, if you are not familiar with this argument, the recommended value is
.
When the value is used it is essential to test the value of ifail on exit.
On exit:
unless the routine detects an error or a warning has been flagged (see
Section 6).
If on entry
or
, explanatory error messages are output on the current error message unit (as defined by
x04aaf).
Not applicable.
Please consult the
X06 Chapter Introduction for information on how to control and interrogate the OpenMP environment used within this routine. Please also consult the
Users' Note for your implementation for any additional implementation-specific information.
None.
This example first calls
g05kff to initialize a base generator then calls
g05pgf to generate two realizations, each consisting of ten observations, from an exponential
model.