Accessor Methods for Mixed-Frequency Models

Access standard model summaries from a fitted mf_model() object.

Usage

# S3 method for class 'mf_model'
coef(object, ...)

# S3 method for class 'mf_model'
confint(object, parm = NULL, level = 0.95, ...)

# S3 method for class 'mf_model'
formula(x, ...)

# S3 method for class 'mf_model'
nobs(object, ...)

# S3 method for class 'mf_model'
vcov(object, ...)

# S3 method for class 'mf_model'
fitted(object, ...)

# S3 method for class 'mf_model'
residuals(object, ...)

# S3 method for class 'mf_model'
model.frame(formula, which = c("estimation", "forecast"), ...)

# S3 method for class 'mf_model'
variable.names(object, which = c("all", "xreg", "target_lags"), ...)

# S3 method for class 'mf_model'
print(x, ...)

Arguments

object, x, formula

A fitted "mf_model" object returned by mf_model(). The formula spelling is required by the stats::model.frame() generic and carries the same meaning.

...

Unused.

parm, level

Passed to confint(). Confidence intervals are computed from the coefficient covariance matrix returned by stats::vcov(), which may be the HAC or Delta-HAC covariance when se = TRUE. Critical values use a t-distribution with residual degrees of freedom from the fitted target equation; this is conservative relative to asymptotic normal critical values but is common practice in applied econometrics.

which

For model.frame(), which modelling frame to return, "estimation" (default) or "forecast". For variable.names(), which group of regressor names to return, "all" (default), "xreg" or "target_lags".

Value

The requested model summary, usually delegated from the stored target regression fit.

x, invisibly.

Details

residuals.mf_model() returns target-equation residuals on the same standardized scale as the fitted target series, so they can be passed directly to downstream residual diagnostics.

model.frame.mf_model() returns the aligned modelling data. Use which = "estimation" for the in-sample bridge-equation data, and which = "forecast" for the future target-period regressor path the forecast is produced from. The latter is the frame to modify and pass back as xreg when constructing scenarios.

variable.names.mf_model() returns the names of the bridge-equation regressors. which = "xreg" returns the non-target-lag regressors, which are exactly the series a custom xreg must supply when forecasting a scenario, and pairs with model.frame(object, which = "forecast").

Examples

gdp_growth <- tsbox::ts_pc(gdp)
#> [value]: 'values' 
#> [value]: 'values' 
gdp_growth <- tsbox::ts_na_omit(gdp_growth)
#> [value]: 'values' 
model <- mf_model(
  target = gdp_growth,
  indic = baro,
  indic_predict = "auto.arima",
  indic_aggregators = "mean",
  h = 1
)

coef(model)
#> (Intercept)        baro 
#>   -9.961997    0.103915