plot_identification
Plot pair-by-pair cross-correlation and partial correlation panels to identify time-series dependence, lead-lag direction, and candidate MA/AR orders before fitting joint VARMA models.
Instructions
FIGURE — Jenkins and Alavi's (1981) identification, pair by pair: the figure of identify_matrices. For each pair of series, in GraphMaker's CCF panel (Treadway's, the one drtran reads), the correlation function R_k above the partial S_k, both two-sided, same lag axis and scale — as art's ACF over PACF.
method=2 (default): the residuals of the univariate models (prewhitened,
each series by ITS OWN model — Haugh's CCF, not a transfer function's).
Band 2/sqrt(n); between the panels Haugh's S* (1976), the test that the
two are independent, in total and by side (k > 0, k < 0: who leads).
The CCF's cut-off after q suggests an MA(q) residual model.
method=1: the stationary series w_t, not prewhitened. The CCF band is
Bartlett's (3.13) for unrelated series, lag by lag: the series are not
white, and a 2/sqrt(n) band would show their common cycles as cross terms.
Here the partial is the decisive one: a cut-off after p suggests an AR(p).
Each panel is titled "A - B", GraphMaker's order: A leads at k > 0. With three or more
series the partial of a pair comes from the VAR of all of them — given the
others — and pairs ("A-B, A-C") draws only those; read identify_matrices'
determinants and + - . table first to choose them.
nlags defaults to max(10, 2 x frequency); the partial stops at the order
the sample supports (n / (3m)), marked on the panel.
Tool call template
Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.
{
"id": 1,
"jsonrpc": "2.0",
"method": "tools/call",
"params": {
"arguments": {
"name": "<name>"
},
"name": "plot_identification"
}
}Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | ||
| path | No | ||
| nlags | No | ||
| pairs | No | ||
| method | No |