| technology_gap_ratio | R Documentation |
Extracts the technology gap ratios (TGR) from a fitted metafrontier model. The TGR measures how close a group's production frontier is to the global metafrontier at each observation's input mix.
technology_gap_ratio(object, by_group = TRUE, ...)
object |
a fitted |
by_group |
logical. If |
... |
additional arguments (currently unused). |
The technology gap ratio is defined as:
TGR_i = \frac{f(x_i; \hat\beta_j)}{f(x_i; \hat\beta^*)}
for the deterministic SFA metafrontier, and
TGR_i = \frac{TE^*_i}{TE^{group}_i}
for DEA-based metafrontiers. Under the stochastic metafrontier of
Huang, Huang, and Liu (2014) the Stage 2 model is
\ln f(x_i; \hat\beta_j) = x_i'\beta^* + v^*_i - u^*_i, and the
TGR is the conditional expectation
TGR_i = E[\exp(-u^*_i) \mid \varepsilon^*_i]
(Battese and Coelli, 1988), so the noise v^*_i is not part of
the technology gap.
In all cases TGR lies in (0, 1].
A TGR of 1 means the group frontier coincides with the metafrontier at that input mix. Values less than 1 indicate a technology gap. Since each group technology is a restricted subset of the common metatechnology (Battese, Rao and O'Donnell, 2004), the gap reflects the restrictions a group faces (regulation, environment, endowments) rather than a fundamentally different technology.
If by_group = TRUE, a named list of numeric vectors.
If by_group = FALSE, a numeric vector of length
nobs(object).
metafrontier, efficiencies.metafrontier
set.seed(42)
sim <- simulate_metafrontier(n_groups = 2, n_per_group = 100)
fit <- metafrontier(log_y ~ log_x1 + log_x2,
data = sim$data, group = "group")
tgr <- technology_gap_ratio(fit)
lapply(tgr, summary)
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