weight_diagnostics(): how concentrated the FGLS weights \(1/\hat v_t\) are: the ratio of the largest to the smallest fitted variance, the Kish effective sample size \((\sum w)^2 / \sum w^2\) as a share of the sample, and the share of the total weight carried by the top 1% of observations.ar_roots(): roots of the estimated lag polynomial \(1 - \hat\rho_1 z - \dots - \hat\rho_k z^k\); the filter is invertible when all lie outside the unit circle.nonexogeneity_share(): the relative size of the first-stage correction of PG26 eq. (29), averaged over coefficients. Values near zero mean the regressors behave as exogenous; large values mean the naive variance (eq. 27) would understate the uncertainty. It is computed from the robust variance whatevervcovthe fit uses, and isNAfor the benchmark estimators (seeols_robust()) and forserial = "ma1", which do not compute it.
Value
weight_diagnostics(): a named numeric vector with elementsvariance_ratio,effective_n_shareandtop1pct_weight_share.ar_roots(): a complex vector with the \(k^*\) roots (empty when \(k^* = 0\)).nonexogeneity_share(): a single number.
See also
Other diagnostics:
adf_test(),
test_predetermined()
Examples
fit <- fgls(y ~ x, data = simulate_combined(200, seed = 1), seed = 1)
weight_diagnostics(fit)
#> variance_ratio effective_n_share top1pct_weight_share
#> 5.57109452 0.81246809 0.02399232
Mod(ar_roots(fit))
#> [1] 1.897991
nonexogeneity_share(fit)
#> [1] 0.000919948