NAG Library Function Document

nag_tsa_transf_prelim_fit (g13bdc)

void	nag_tsa_transf_prelim_fit (double r0, const double r[], Integer nl, Nag_TransfOrder transfv, double s, double wds[], Integer isf[], NagError fail)

3 Description

nag_tsa_transf_prelim_fit (g13bdc) calculates estimates of parameters

δ_{1}, δ_{2}, \dots, δ_{p}

ω_{0}, ω_{1}, \dots, ω_{q}

in the transfer function model

y_{t} = δ_{1} y_{t - 1} + δ_{2} y_{t - 2} + \dots + δ_{p} y_{t - p} + ω_{0} x_{t - b} - ω_{1} x_{t - b - 1} - \dots - ω_{q} x_{t - b - q}

given cross-correlations between the series

x_{t}

and lagged values of

y_{t}

r_{x y} (l), l = 0, 1, \dots, L

and the ratio of standard deviations

s_{y} / s_{x}

, as supplied by nag_tsa_cross_corr (g13bcc).

It is assumed that the series

x_{t}

used to calculate the cross-correlations is a sample from a time series with true autocorrelations of zero. Otherwise the cross-correlations between the series

b_{t}

and

a_{t}

, as defined in the description of nag_tsa_arma_filter (g13bac), should be used in place of those between

y_{t}

and

x_{t}

The estimates are obtained by solving for

δ_{1}, δ_{2}, \dots, δ_{p}

the equations

r_{x y} (b + q + j) = δ_{1} r_{x y} (b + q + j - 1) + \dots + δ_{p} r_{x y} (b + q + j - p), j = 1, 2, \dots, p

then calculating

ω_{i} = \pm (s_{y} / s_{x}) [r_{x y} (b + i) - δ_{1} r_{x y} (b + i - 1) - \dots - δ_{p} r_{x y} (b + i - p)], i = 0, 1, \dots, q

where the ‘

+

’ is used for

ω_{0}

and ‘

-

’ for

ω_{i}

i > 0

Any value of

r_{x y} (l)

arising in these equations for

l < b

is taken as zero. The parameters

δ_{1}, δ_{2}, \dots, δ_{p}

are checked as to whether they satisfy the stability criterion.

4 References

Box G E P and Jenkins G M (1976) Time Series Analysis: Forecasting and Control (Revised Edition) Holden–Day

5 Arguments

1: r0 – doubleInput

On entry: the cross-correlation between the two series at lag

0

r_{x y} (0)

Constraint:

- 1.0 \leq r0 \leq 1.0

2: r[nl] – const doubleInput

On entry: the cross-correlations between the two series at lags

1

L

r_{x y} (l)

, for

l = 1, 2, \dots, L

Constraint:

- 1.0 \leq r [i] \leq 1.0

, for

i = 0, 1, \dots, nl - 1

3: nl – IntegerInput

On entry:

L

, the number of lagged cross-correlations in the array r.

Constraint:

nl \geq \max (transfv . nag_b + transfv . nag_q + transfv . nag_p, 1)

4: transfv – Nag_TransfOrder *Input

On entry: the orders of the transfer function model where the triplet (transfv. nag_b, transfv. nag_q, transfv. nag_p) corresponds to the triplet

(b, q, p)

as described in Section 2.3.1 in the g13 Chapter Introduction.

Constraints:

$transfv . nag_b \geq 0$ ;
$transfv . nag_q \geq 0$ ;
$transfv . nag_p \geq 0$ .

5: s – doubleInput

On entry: the ratio of the standard deviation of the

y

series to that of the

x

series,

s_{y} / s_{x}

Constraint:

s > 0.0

6: wds[ $\dim$ ] – doubleOutput

Note: the dimension, dim, of the array wds must be at least

(transfv . nag_q + transfv . nag_p + 1)

On exit: the preliminary estimates of the parameters of the transfer function model in the order of

q + 1

MA-like parameters followed by the

p

AR-like parameters. If the estimation of either type of parameter fails then these arguments are set to

0.0

7: isf[ $2$ ] – IntegerOutput

On exit: indicators of the success of the estimation of MA-like and AR-like parameters respectively. A value

0

indicates that there are no parameters of that type to be estimated. A value of

1

- 1

indicates that there are parameters of that type in the model and the estimation of that type has been successful or unsuccessful respectively. Note that there is always at least one MA-like parameter in the model.

8: fail – NagError *Input/Output

The NAG error argument (see Section 3.6 in the Essential Introduction).

6 Error Indicators and Warnings

NE_ALLOC_FAIL: Dynamic memory allocation failed.
NE_BAD_PARAM: On entry, argument $⟨value⟩$ had an illegal value.
NE_CONSTRAINT: On entry, $transfv = ⟨value⟩$ .
Constraint: $transfv . nag_b \geq 0$ .
On entry, $transfv = ⟨value⟩$ .
Constraint: $transfv . nag_p \geq 0$ .
On entry, $transfv = ⟨value⟩$ .
Constraint: $transfv . nag_q \geq 0$ .
NE_INT_4: On entry, $nl = ⟨value⟩$ , $transfv . nag_b = ⟨value⟩$ , $transfv . nag_q = ⟨value⟩$ and $transfv . nag_p = ⟨value⟩$ .
Constraint: $nl \geq \max (transfv . nag_b + transfv . nag_q + transfv . nag_p, 1)$ .
NE_INTERNAL_ERROR: An internal error has occurred in this function. Check the function call and any array sizes. If the call is correct then please contact NAG for assistance.
NE_REAL: On entry, r0 lies outside $[- 1.0, 1.0]$ : $r0 = ⟨value⟩$ .
On entry, $s = ⟨value⟩$ .
Constraint: $s > 0.0$ .
NE_REAL_ARRAY_ELEM_CONS: On entry, $r [I - 1]$ lies outside $[- 1.0, 1.0]$ : $I = ⟨value⟩$ and $r [I - 1] = ⟨value⟩$ .

7 Accuracy

Equations used in the computations may become unstable, in which case results are reset to zero with array isf values set accordingly.

8 Parallelism and Performance

Not applicable.

9 Further Comments

The time taken by nag_tsa_transf_prelim_fit (g13bdc) is roughly proportional to

{(transfv . nag_q + transfv . nag_p + 1)}^{3}

10 Example

This example reads the cross-correlations between two series at lags

0

6

. It then reads a

(3, 2, 1)

transfer function model and calculates and prints the preliminary estimates of the parameters of the model.

NAG Library Function Documentnag_tsa_transf_prelim_fit (g13bdc)

+− Contents

1 Purpose

2 Specification

3 Description

4 References

5 Arguments

6 Error Indicators and Warnings

7 Accuracy

8 Parallelism and Performance

9 Further Comments

10 Example

10.1 Program Text

10.2 Program Data

10.3 Program Results

NAG Library Function Document

nag_tsa_transf_prelim_fit (g13bdc)