Summarize a pangmm fit
Usage
# S3 method for class 'pangmm'
summary(object, time.dummies = FALSE, ...)Value
an object of class "summary.pangmm": a list with the
coefficient table (coefficients: estimate, standard error, z value
and p-value), the Arellano-Bond tests (artest), sargan, hansen,
wald and the fit information (call, label, vcov_type, nobs,
n_groups, n_periods, n_instruments).
See also
Other pangmm methods:
plot.pangmm(),
predict.pangmm(),
vcov.pangmm()
Examples
set.seed(3)
N <- 200; TT <- 6
d <- data.frame(id = rep(seq_len(N), each = TT), t = rep(seq_len(TT), N))
mu <- rep(rnorm(N), each = TT)
d$y <- ave(mu + rnorm(N * TT), d$id, FUN = function(e) stats::filter(e, 0.4,
method = "recursive"))
summary(pangmm(y ~ lag(y, 1) | lag(y, 2:99), data = d, index = c("id", "t")))
#>
#> Call:
#> pangmm(formula = y ~ lag(y, 1) | lag(y, 2:99), data = d, index = c("id",
#> "t"))
#>
#> Estimator: two-way one-step difference GMM (Arellano-Bond)
#> n = 800 groups = 200 T = 6 instruments = 14
#> vcov: robust
#>
#> Coefficients:
#> Estimate Std. Error z value Pr(>|z|)
#> lag(y, 1) 0.3645 0.1268 2.875 0.00404 **
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Arellano-Bond AR(1) test: z = -4.564, p = 5.018e-06
#> Arellano-Bond AR(2) test: z = -0.4769, p = 0.6334
#> Sargan test (not robust): chi2(9) = 7.899, p = 0.5443
#> Hansen J test (robust): chi2(9) = 9.133, p = 0.4251
#> Wald test for coefficients: chi2(1) = 8.264, p = 0.004043
#>