prais extracts the regression, which is an object of class "praislm", of a
twoStepsBenchmark object.
Value
prais returns an object of class "praislm".
The functions that can be used on that class are almost the same than
for the class twoStepsBenchmark.
summary, coefficients, residuals will return the same values.
However, as for fitted.values, the accessor returns the fitted values
of the regression, not the high-frequency, eventually integrated, time series
contained in a twoStepsBenchmark.
An object of class "praislm" is a list containing the following components :
- coefficients
a named vector of coefficients.
- residuals
the residuals, that is response minus fitted values.
- fitted.values
a time series, the fitted mean values
- se
a named vector of standard errors.
- df.residuals
the residual degrees of freedom.
- rho
the autocorrelation coefficients of the residuals. It is equal to zero if twoStepsBenchmark was called with
include.rho=FALSE- residuals.decorrelated
the residuals of the model after having been transformed by rho in a least square model.
- fitted.values.decorrelated
the fitted values of the model after having been transformed by rho in a least square model.
Examples
benchmark <- twoStepsBenchmark(turnover,construction); prais(benchmark)
#>
#> Call:
#> twoStepsBenchmark(hfserie = turnover, lfserie = construction)
#>
#> Coefficients:
#> constant hfserie
#> 44.282 0.141
#>