summary.prais {prais}

Summarising the Prais-Winsten Estimator

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 \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.