forecast_uncertainty
Compares forecast uncertainty by lead time between a fitted multivariate VARMA model and univariate benchmarks, highlighting where joint modeling could improve forecasts.
Instructions
N5/N6 — Jenkins and Alavi's comparison of forecast uncertainty (their
Table VIII): the standard deviation of the forecast errors by lead time,
V(l) = SUM psi_j Sigma psi_j', of the last estimated model against the
univariate models, per cent for series in logs. In sample, parameters
taken as known: a quick reading of where the multivariate model could
help; evaluate is the test. horizon defaults to the frequency (4 for
annual data).
estwin (optional): re-estimate the model AND the univariate models on
the first estwin observations of the first series, as Jenkins and Alavi
did for their Table VIII (§6.3: the muskrat-mink models refitted on 48
observations, the rest withheld). 0 = the last fit, on all the data.
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": "forecast_uncertainty"
}
}Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | ||
| estwin | No | ||
| horizon | No |
Output Schema
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |