Methods for fgls fits

Standard accessors for objects returned by fgls(), serial_fgls(), lasso_fgls() and the benchmark estimators. They make the fits work with generic tools such as lmtest::coeftest() and lmtest::coefci().

Usage

# S3 method for class 'fgls'
vcov(object, type = NULL, ...)

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

# S3 method for class 'fgls'
residuals(object, type = c("response", "innovations"), ...)

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

# S3 method for class 'fgls'
predict(
  object,
  newdata,
  se.fit = FALSE,
  interval = c("none", "confidence"),
  level = 0.95,
  type = "response",
  na.action = stats::na.pass,
  ...
)

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

# S3 method for class 'fgls'
df.residual(object, ...)

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

# S3 method for class 'fgls'
model.matrix(object, ...)

# S3 method for class 'fgls'
model.frame(formula, ...)

# S3 method for class 'fgls'
terms(x, ...)

# S3 method for class 'fgls'
print(x, digits = max(3L, getOption("digits") - 3L), ...)

Arguments

object, x, formula: An "fgls" object.
type: For vcov() and confint(): NULL (the variance chosen when fitting) or another variance type to compute from the stored estimates: "robust", "naive", "nonrobust", "HC0"–"HC3" (where available) or "bootstrap" (re-runs the estimator); see the section Variance types. For residuals(): "response" ($y_t - x_t'\hat\beta$, full sample) or "innovations" (residuals of the final filtered and/or weighted regression). For predict(): only "response" (the mean of the response) is supported.
...: Not used.
parm: Coefficients (names or positions); all by default.
level: Confidence level.
newdata: Optional data frame in which to look for the regressors. Without it, predictions are for the observations of the fit, padded with NA for the rows dropped by stats::na.exclude().
se.fit: Return standard errors of the predicted means.
interval: "none" or "confidence" (for the mean of the response).
na.action: Missing-value handling for newdata.
digits: Number of significant digits to print.

Value

The usual values: a coefficient covariance matrix (vcov), a matrix of confidence limits (confint), vectors of residuals or fitted values, predictions (a vector, or a list/matrix with se.fit/interval), or the corresponding model components. The print() method returns x invisibly.

Details

Inference is asymptotically normal (as in the papers) unless the fit was made with use_t = TRUE. Accordingly, df.residual() returns Inf for normal inference, so that lmtest::coeftest() reproduces summary.fgls(), and $n_{eff} - q$ otherwise. nobs() returns the number of observations in the final regression, $n_{eff} = T - k^*$.

Variances and the sandwich package

The variance estimators of the papers are available through vcov(fit, type = ...) and can be passed to other tools (for example lmtest::coeftest(fit, vcov. = vcov(fit, type = "naive"))). The package provides no sandwich::estfun() or sandwich::bread() methods, because the robust variance of PG26 eq. (29) and the bias-corrected sandwich of GP24 eq. (8) are not of the generic form those functions assume: sandwich::vcovHAC() and related functions would not reproduce them.

Variance types

vcov(fit, type = t) returns the stored covariance when t is the type the fit was made with (e.g. "robust", or "HAC" for ols_robust(), whose stored vcov_type label also shows the kernel and bandwidth). Other types are recomputed from the stored estimates only for fits made by fgls(), serial_fgls() and lasso_fgls(), and for the F-C stage of a combined fit (fit$stages$serial, types "robust", "HC0" and "HC3"). The benchmark estimators store only the covariance they were fitted with; refit them with another vcov instead.

Examples

sim <- simulate_serial(200, seed = 1)
fit <- serial_fgls(y ~ x, data = sim)
vcov(fit)
#>               (Intercept)             x
#> (Intercept)  1.282794e-01 -7.488239e-05
#> x           -7.488239e-05  1.369205e-03
vcov(fit, type = "naive")
#>               (Intercept)             x
#> (Intercept)  1.282722e-01 -8.898623e-05
#> x           -8.898623e-05  1.341769e-03
confint(fit)
#>                 2.5 %   97.5 %
#> (Intercept) 0.4984541 1.902420
#> x           0.9078455 1.052894
predict(fit, newdata = data.frame(x = c(-1, 0, 1)), interval = "confidence")
#>         fit        lwr       upr
#> 1 0.2200675 -0.4860593 0.9261944
#> 2 1.2004371  0.4984541 1.9024201
#> 3 2.1808067  1.4754950 2.8861183
head(residuals(fit, type = "innovations"))
#>          2          3          4          5          6          7 
#> -0.8577166  1.5184545  0.2807810 -0.8097611  0.4004779  0.7147435 
nobs(fit)
#> [1] 199