Summarize a Mixed-Frequency Model

Computes the summary quantities for a fitted mf_model() object and returns them as a "summary.mf_model" object. Following the convention of summary.lm(), the summary is a data object in its own right: the report is rendered by print.summary.mf_model() rather than by summary() itself, so the individual quantities can be extracted programmatically.

Usage

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

Arguments

object

A "mf_model" object returned by mf_model().

...

Unused.

Value

An object of class "summary.mf_model", a list with components:

target_name, target_frequency, h, nobs

Target series name, inferred target frequency unit, forecast horizon, and number of estimation rows.

regressor_names

Character vector of bridge-equation regressors.

coefficients

Numeric matrix with columns "Estimate", "Std. Error", "t value" and "Pr(>|t|)". Standard errors come from vcov.mf_model(), so they are the HAC, Delta-HAC or bootstrap standard errors when the model was fitted with se = TRUE.

coefficient_method

Method used for the coefficient standard errors, or NULL when the model was fitted without uncertainty.

r.squared, adj.r.squared, sigma, df.residual

Fit measures for the target equation.

indicators

Data frame of per-indicator frequency, completion method and aggregation scheme, one row per indicator.

custom_weights

Named list of user-supplied numeric aggregation weights, empty when none were used.

parametric_weights, parametric_parameters

Named lists of estimated parametric aggregation weights and their underlying parameters, empty when no parametric aggregator was used.

uncertainty, bootstrap, optimization

Uncertainty settings, bootstrap diagnostics, and joint parametric-optimization diagnostics.

See also

coef.mf_model(), confint.mf_model() and vcov.mf_model() for extracting individual quantities directly from the fitted model.

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
)

model_summary <- summary(model)
model_summary
#> Mixed-frequency model summary
#> -----------------------------------
#> Target series: gdp_growth
#> Target frequency: quarter
#> Forecast horizon: 1
#> Estimation rows: 75
#> Regressors: baro
#> -----------------------------------
#> Target equation coefficients:
#>             Estimate
#> (Intercept)   -9.962
#> baro           0.104
#> -----------------------------------
#> Model fit:
#>  Statistic               Value
#>  R-squared               0.477
#>  Adjusted R-squared      0.469
#>  Residual standard error 0.967
#> -----------------------------------
#> Indicator summary:
#>      Frequency Predict    Aggregation
#> baro month     auto.arima mean       
#> -----------------------------------

# The coefficient matrix is available programmatically, as for `lm()`.
coef(model_summary)
#>              Estimate Std. Error   t value     Pr(>|t|)
#> (Intercept) -9.961997 1.28968665 -7.724355 4.635357e-11
#> baro         0.103915 0.01274652  8.152423 7.281639e-12