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'
print(x, ...)

Arguments

object, x

A fitted "mf_model" object returned by mf_model().

...

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.

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.

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