Predictions from a dynamic panel GMM fit

Linear predictions from a pangmm() fit on newdata, a long data frame with the panel index columns and the variables of the first part of the formula. Lags are taken within individuals over the sorted periods of newdata, as in the fit.

Usage

# S3 method for class 'pangmm'
predict(object, newdata, type = c("level", "difference"), ...)

Arguments

object: a "pangmm" fit
newdata: a data frame in long format; the fit does not store its data, so newdata is required (pass the fitting data for in-sample predictions)
type: "level" or "difference"
...: unused

Value

a numeric vector with one prediction per row of newdata

Details

type = "level": x_it'b + d_t (+ c), the regressors (with their lags, including the lagged dependent variable) times their coefficients, plus the time effect and, for system GMM, the constant. The individual effect is not identified by GMM and is excluded, so for difference GMM (no constant) this is the level up to an individual intercept. On the fitting data these are the fitted values of the level equations of system GMM.
type = "difference": the prediction of the first difference y_it - y_i,t-1, the difference of the level predictions, in which the individual effect cancels. On the fitting data these are the fitted values of the differenced equations.

Periods that precede the first time effect of the fit have a time effect of zero (the reference); a later period that is not in the fit gives a missing prediction, with a warning. A prediction is missing whenever a required lag is unavailable.

Validation

Numerical accuracy is checked in the package tests and in the validation suite; see the validation article on the package website.

Examples

set.seed(3)
N <- 100; TT <- 6
d <- data.frame(id = rep(seq_len(N), each = TT), t = rep(seq_len(TT), N))
d$x <- rnorm(N * TT)
d$y <- ave(rep(rnorm(N), each = TT) + 0.3 * d$x + rnorm(N * TT), d$id,
           FUN = function(e) stats::filter(e, 0.4, method = "recursive"))
fit <- pangmm(y ~ lag(y, 1) + x | lag(y, 2:99), data = d, index = c("id", "t"),
              transformation = "ld")
head(predict(fit, newdata = d))
#> [1]         NA -0.6878707 -0.8761411 -1.0115302  0.1196619 -0.5116195
head(predict(fit, newdata = d, type = "difference"))
#> [1]         NA         NA -0.1882704 -0.1353892  1.1311921 -0.6312814