Compare fits side by side

A table of coefficients, standard errors and confidence-interval widths for several fits, e.g. FGLS against OLS with HAC standard errors (PG26) or OLS with HC3 standard errors (GP24), or the F-C and F-CH stages of a combined fit.

Usage

compare_fgls(..., level = 0.95, ols_vcov = NULL)

Arguments

...

"fgls" (or plain "lm") objects, optionally named; the names label the columns.

level

Confidence level used for the interval widths.

ols_vcov

Covariance type for lm fits: NULL (automatic), "HAC", "NW" or "HC0"–"HC3".

Value

A data frame with columns term, statistic ("estimate", "std.error", "ci.width") and one column per fit.

Details

Plain stats::lm() fits are accepted and converted to OLS with HAC (Andrews quadratic-spectral) standard errors when any other fit corrects for serial correlation, and with HC3 standard errors otherwise; override with ols_vcov. Other models that inherit from "lm" (glm, multivariate mlm fits, weighted fits, fits with an offset, ...) are rejected, because the conversion would replace them by unweighted OLS; use ols_robust() for OLS with robust standard errors.

See also

Other fgls: fgls(), fgls-methods, fgls_control(), lasso_fgls(), plot.fgls(), serial_fgls(), summary.fgls()

Examples

sim <- simulate_serial(300, seed = 1)
compare_fgls(
  OLS = stats::lm(y ~ x, data = sim),
  FGLS = serial_fgls(y ~ x, data = sim)
)
#>          term statistic        OLS       FGLS
#> 1 (Intercept)  estimate 1.16317984 1.18739202
#> 2 (Intercept) std.error 0.11500951 0.23736465
#> 3 (Intercept)  ci.width 0.45082901 0.93045231
#> 4           x  estimate 1.34140597 0.98639665
#> 5           x std.error 0.06851676 0.03201629
#> 6           x  ci.width 0.26858077 0.12550157