summary.prais: Summarising the Prais-Winsten Estimator

In prais: Prais-Winsten Estimator for AR(1) Serial Correlation

Description

Summary method for class "prais".

Usage

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

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

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.

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 ρ 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{∑ {(y_i - \hat{y}_i)^2}}{∑ {(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.

prais documentation built on Nov. 1, 2021, 5:07 p.m.