Tidy a(n) varest object

Tidy summarizes information about the components of a model. A model component might be a single term in a regression, a single hypothesis, a cluster, or a class. Exactly what tidy considers to be a model component varies across models but is usually self-evident. If a model has several distinct types of components, you will need to specify which components to return.

Usage

# S3 method for class 'varest'
tidy(x, conf.int = FALSE, conf.level = 0.95, ...)

Arguments

x

A varest object produced by a call to vars::VAR().

conf.int

Logical indicating whether or not to include a confidence interval in the tidied output. Defaults to FALSE.

conf.level

The confidence level to use for the confidence interval if conf.int = TRUE. Must be strictly greater than 0 and less than 1. Defaults to 0.95, which corresponds to a 95 percent confidence interval.

...

For glance(), additional arguments passed to summary(). Otherwise ignored.

Details

The tibble has one row for each term in the regression. The component column indicates whether a particular term was used to model either the "mean" or "precision". Here the precision is the inverse of the variance, often referred to as phi. At least one term will have been used to model the precision phi.

The vars package does not include a confint method and does not report confidence intervals for varest objects. Setting the tidy argument conf.int = TRUE will return a warning.

See also

tidy(), vars::VAR()

Value

A tibble::tibble() with columns:

conf.high

Upper bound on the confidence interval for the estimate.

conf.low

Lower bound on the confidence interval for the estimate.

estimate

The estimated value of the regression term.

p.value

The two-sided p-value associated with the observed statistic.

statistic

The value of a T-statistic to use in a hypothesis that the regression term is non-zero.

std.error

The standard error of the regression term.

term

The name of the regression term.

component

Whether a particular term was used to model the mean or the precision in the regression. See details.

Examples

# load libraries for models and data
library(vars)

# load data
data("Canada", package = "vars")

# fit models
mod <- VAR(Canada, p = 1, type = "both")

# summarize model fit with tidiers
tidy(mod)
#> # A tibble: 24 × 6
#>    group term     estimate std.error statistic  p.value
#>    <chr> <chr>       <dbl>     <dbl>     <dbl>    <dbl>
#>  1 e     e.l1       1.24      0.0863    14.4   1.82e-23
#>  2 e     prod.l1    0.195     0.0361     5.39  7.49e- 7
#>  3 e     rw.l1     -0.0678    0.0283    -2.40  1.90e- 2
#>  4 e     U.l1       0.623     0.169      3.68  4.30e- 4
#>  5 e     const   -279.       75.2       -3.71  3.92e- 4
#>  6 e     trend     -0.0407    0.0197    -2.06  4.24e- 2
#>  7 prod  e.l1       0.0129    0.126      0.103 9.19e- 1
#>  8 prod  prod.l1    0.963     0.0527    18.3   9.43e-30
#>  9 prod  rw.l1     -0.0391    0.0412    -0.948 3.46e- 1
#> 10 prod  U.l1       0.211     0.247      0.855 3.95e- 1
#> # ℹ 14 more rows
glance(mod)
#> # A tibble: 1 × 4
#>   lag.order logLik  nobs     n
#>       <dbl>  <dbl> <dbl> <dbl>
#> 1         1  -208.    83    84