Recursive Augmented Dickey-Fuller

Tidy into a joint model

tidy_join.radf_obj

tidy_join(x, y = NULL, ...)

Tidy or augment and then join objects of class radf_obj and radf_cv. The radf_cv is the object of reference. For example, if you provide panel critical values, the function returns the panel test statistic.

Arguments

x	An object of class `radf_obj`.
y	An object of class `radf_cv`. The output will depend on the type of critical value.
...	Further arguments passed to methods. Not used.

Details

tidy_join also calls augment_join when cv is of class sb_cv.

Examples

These examples are copied from the package's own documentation and are run by R CMD check on every release. The printed output (after #>) and the plots were produced by running them against the current package source.

rsim_data <- radf(sim_data, minw = 20)
cv <- radf_wb_cv(sim_data, minw = 20)

# One row per series/statistic, statistic and critical value side by side
tidy_join(rsim_data, cv)
#> # A tibble: 45 × 5
#>    id    stat  tstat sig      crit
#>    <fct> <fct> <dbl> <fct>   <dbl>
#>  1 psy1  adf   -2.46 90    -0.596 
#>  2 psy1  adf   -2.46 95    -0.362 
#>  3 psy1  adf   -2.46 99     0.0523
#>  4 psy1  sadf   1.95 90     1.51  
#>  5 psy1  sadf   1.95 95     1.97  
#>  6 psy1  sadf   1.95 99     2.87  
#>  7 psy1  gsadf  5.19 90     2.78  
#>  8 psy1  gsadf  5.19 95     3.33  
#>  9 psy1  gsadf  5.19 99     4.09  
#> 10 psy2  adf   -2.86 90    -0.629 
#> # ℹ 35 more rows

# summary() and diagnostics() are themselves built on top of tidy_join()
summary(rsim_data, cv = cv)
#> 
#> ── Summary (minw = 20, lag = 0) ──────────────── Wild Bootstrap (nboot = 500) ──
#> 
#> psy1 :
#> # A tibble: 3 × 5
#>   stat  tstat   `90`   `95`   `99`
#>   <fct> <dbl>  <dbl>  <dbl>  <dbl>
#> 1 adf   -2.46 -0.596 -0.362 0.0523
#> 2 sadf   1.95  1.51   1.97  2.87  
#> 3 gsadf  5.19  2.78   3.33  4.09  
#> 
#> psy2 :
#> # A tibble: 3 × 5
#>   stat  tstat   `90`   `95`   `99`
#>   <fct> <dbl>  <dbl>  <dbl>  <dbl>
#> 1 adf   -2.86 -0.629 -0.530 -0.251
#> 2 sadf   7.88  3.07   3.88   5.59 
#> 3 gsadf  7.88  3.99   4.68   5.98 
#> 
#> evans :
#> # A tibble: 3 × 5
#>   stat  tstat   `90`   `95`    `99`
#>   <fct> <dbl>  <dbl>  <dbl>   <dbl>
#> 1 adf   -5.83 -0.668 -0.451 -0.0787
#> 2 sadf  -2.73  5.00   6.38  12.6   
#> 3 gsadf  5.47  7.25   9.22  13.2   
#> 
#> div :
#> # A tibble: 3 × 5
#>   stat  tstat   `90`   `95`  `99`
#>   <fct> <dbl>  <dbl>  <dbl> <dbl>
#> 1 adf   -1.95 -0.548 -0.121 0.615
#> 2 sadf   1.11  0.972  1.15  1.69 
#> 3 gsadf  1.11  1.65   2.07  2.77 
#> 
#> blan :
#> # A tibble: 3 × 5
#>   stat  tstat   `90`   `95`   `99`
#>   <fct> <dbl>  <dbl>  <dbl>  <dbl>
#> 1 adf   -5.15 -0.223 0.0861  0.479
#> 2 sadf   3.93  3.07  4.23    6.02 
#> 3 gsadf 11.0   6.66  7.67   12.2