summary.prais: Summarising the Prais-Winsten Estimator

Description

Summary method for class "prais".

Usage

# S3 method for prais
summary(object, ...)
# S3 method for summary.prais
print(
  x,
  digits = max(3L, getOption("digits") - 3L),
  signif.stars = getOption("show.signif.stars"),
  ...
)

Value

summary.prais returns a list of class "summary.prais", which contains the following components:

call: the matched call.
residuals: the residuals, that is the response minus the fitted values.
coefficients: a named vector of coefficients.
rho: the values of the AR(1) coefficient $\rho$ from all iterations.
sigma: the square root of the estimated variance of the random error.
df: degrees of freedom, a 3-vector (p, n-p, p*), the first being the number of non-aliased coefficients, the last being the total number of coefficients.
r.squared: R^2, the 'fraction of variance explained by the model', $$R^2 = 1 - \frac{\sum {(y_i - \hat{y}_i)^2}}{\sum {(y_i - \overline{y})^2}},$$ where $\overline{y}$ is the mean of $y_i$ for $y_i = 1, ..., N$ if there is an intercept and zero otherwise.
adj.r.squared: the above R^2 statistic 'adjusted', penalising for higher p.
fstatistic: (for models including non-intercept terms) a 3-vector with the value of the F-statistic with its numerator and denominator degrees of freedom.
cov.unscaled: a $p \times p$ matrix of (unscaled) covariances of the coef[j], j=1, ..., p.
dw: a named 2-vector with the Durbin-Watson statistic of the original linear model and the Prais-Winsten estimator.
index: a character specifying the ID and time variables.

Arguments

object: an object of class "prais", usually, a result of a call to prais_winsten.
...: further arguments passed to or from other methods.
x: an object of class "summary.prais", usually, a result of a call to summary.prais.
digits: the number of significant digits to use when printing.
signif.stars: logical. If TRUE, 'significance stars' are printed for each coefficient.