Provides a detailed summary of the CSPA test, including per-competitor diagnostics of the estimated conditional mean functions.
Usage
# S3 method for class 'cspa_test'
summary(object, digits = 4, ...)Arguments
- object
An object of class
"cspa_test", as returned bycspa_test.- digits
Integer; number of decimal places. Default
4.- ...
Additional arguments (currently ignored).
Examples
# \donttest{
sim <- do_sim(J = 3, n = 250, a = 1, c = 0, rho_u = 0.4)
result <- cspa_test(sim$Y, sim$X, level = 0.05, trim = 2, R = 500L)
summary(result)
#>
#> ╭────────────────────────────────────────────────────╮
#> │ Conditional Superior Predictive Ability │
#> │ (Li, Liao, and Quaedvlieg, 2022) │
#> ├────────────────────────────────────────────────────┤
#> │ H0: Benchmark weakly dominates all competitors │
#> │ conditionally, uniformly across all states │
#> │ H1: Some competitor outperforms the benchmark │
#> │ in certain conditioning states │
#> ├┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┤
#> │ Test Results: │
#> │ Theta: 0.3200 │
#> │ P-value: 0.9220 │
#> │ Significance level: 0.0500 │
#> │ Decision: Not rejected │
#> ├┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┤
#> │ Estimation Details: │
#> │ Observations (n): 238 │
#> │ Competitors (J): 3 │
#> │ Series terms (K): 4 │
#> │ HAC lag order: 0 (Newey-West) │
#> │ Selected (j,x) pairs: 714 / 714 (100.0%) │
#> ╰────────────────────────────────────────────────────╯
#>
#> Per-competitor diagnostics:
#>
#> Competitor min h_j max h_j mean h_j Selected %
#> --------------------------------------------------------
#> j = 1 -0.1593 1.1349 0.2330 100.0%
#> j = 2 0.0019 1.5655 0.3117 100.0%
#> j = 3 -0.0114 2.1822 0.5327 100.0%
#>
# }