Extract IV diagnostic tests as a tibble

Flattens the IV specification tests stored on a fitted model into a single tibble, one row per test, so they can be filtered, joined, or printed like any other tidyverse output instead of navigated as a nested list (x$diagnostics$overid$stat and friends).

Usage

diagnostics(x, ...)

# S3 method for class 'ivreg2'
diagnostics(x, ...)

Arguments

x

A fitted model object.

...

Additional arguments (ignored).

Value

A tibble::tibble() with one row per diagnostic test present on the model and the following columns:

test

Character. A stable machine-readable key, meant for filter(test == ...)-style selection (e.g. "overid", "weak_id_robust", "endogeneity").

test_name

Character. The display name printed by summary().

statistic

Double. The test statistic, or — for the Stock-Yogo rows — the critical value rather than a statistic.

df

Integer. Degrees of freedom, NA when not applicable.

df2

Integer. Denominator degrees of freedom, NA unless the statistic is F-distributed.

p_value

Double. NA when not defined (e.g. Stock-Yogo critical values, or an exactly identified overidentification row).

tested_vars

Character. The variable(s) tested; populated only for the endogeneity, orthogonality, and redundancy rows.

note

Character. A Stata-parity disclosure, or NA when none applies. A note appears when an explicit option the user set is silently not honored inside an automatically computed diagnostic, matching Stata's ivreg2: a non-Bartlett kernel on the identification and redundancy tests (Stata's ranktest hard-codes Bartlett); kiefer on the identification tests (the Kiefer VCE structure is dropped); psd on the identification and redundancy tests (ranktest never receives it); center on the endogeneity test; and method = "cue" (or "liml", for orthogonality) on the endogeneity and orthogonality C-statistics, which come from a recursive re-estimation that does not use those estimators. When more than one disclosure applies to the same test, the note column joins the sentences with a space; each note is a complete sentence, so splitting on sentence boundaries recovers the individual disclosures programmatically.

Rows appear only for the tests actually computed on the model: an IV fit reports underidentification, weak identification, overidentification, and — for every endogenous regressor, unless narrowed to a subset via endog = — the endogeneity test by default, while orthog = and redundant = add the corresponding orthogonality and redundancy rows only when the user requests them. The Stock-Yogo rows (keys of the form "sy_iv_size_10") report critical values, not test statistics, so their p_value is always NA. An OLS fit (single-part formula) has no diagnostics at all and returns a zero-row tibble with the columns above.

The note column is unique to this accessor: tidy() and glance() keep their numeric-only tidy semantics and carry no disclosure text, so diagnostics() is the surface that exposes a note programmatically. The same notes are printed by summary() as footnotes below the diagnostics block.

Small-sample correction

the overidentification row (Sargan/Hansen J) and the endogeneity row (C-statistic) are never small-sample corrected, regardless of the main model's small argument. The first-stage F-statistics are always small-sample corrected, regardless of small; they are not columns of this tibble but are reached via first_stage(). All three match Stata's ivreg2. See ivreg2r-conventions for the full statement of which corrections small controls.

See also

first_stage(), glance.ivreg2()

Other ivreg2 methods: coef.ivreg2(), confint.ivreg2(), first_stage(), fitted.ivreg2(), formula.ivreg2(), ivreg2(), model.matrix.ivreg2(), nobs.ivreg2(), predict.ivreg2(), print.ivreg2(), print.summary.ivreg2(), residuals.ivreg2(), summary.ivreg2(), terms.ivreg2(), update.ivreg2(), vcov.ivreg2()

Examples

data(card)
fit <- ivreg2(
  lwage ~ exper + expersq + black + south | educ | nearc4 + nearc2,
  data = card, vcov = "robust"
)
diagnostics(fit)
#> # A tibble: 12 × 8
#>    test              test_name statistic    df   df2   p_value tested_vars note 
#>    <chr>             <chr>         <dbl> <int> <int>     <dbl> <chr>       <chr>
#>  1 underid           Kleiberg…     40.1      2    NA  1.98e- 9 NA          NA   
#>  2 weak_id           Cragg-Do…     19.7     NA    NA NA        NA          NA   
#>  3 weak_id_robust    Kleiberg…     20.4     NA    NA NA        NA          NA   
#>  4 sy_iv_size_10     Stock-Yo…     19.9     NA    NA NA        NA          NA   
#>  5 sy_iv_size_15     Stock-Yo…     11.6     NA    NA NA        NA          NA   
#>  6 sy_iv_size_20     Stock-Yo…      8.75    NA    NA NA        NA          NA   
#>  7 sy_iv_size_25     Stock-Yo…      7.25    NA    NA NA        NA          NA   
#>  8 overid            Hansen J       1.86     1    NA  1.72e- 1 NA          NA   
#>  9 anderson_rubin_f  Anderson…     27.9      2  3003  1.04e-12 NA          NA   
#> 10 anderson_rubin_c… Anderson…     55.8      2    NA  7.52e-13 NA          NA   
#> 11 stock_wright      Stock-Wr…     54.0      2    NA  1.92e-12 NA          NA   
#> 12 endogeneity       Endogene…     27.0      1    NA  2.04e- 7 educ        NA