Description Usage Arguments Details Value Author(s) References Examples
Bootstrap estimates of confidence regions for the bounds on model coefficients.
1 2 3 |
object |
Fitted model of class |
parm |
Names or indices of the coefficients for which to estimate confidence sets. If not specified, uses all coefficients. |
level |
Confidence level. |
type |
Whether to return the DU confidence set or the CC confidence collections. See Details for explanation. |
... |
Not used. |
Two types of confidence region are available, both defined in Beresteanu and
Molinari (2008, Section 2). The default is "DU", the confidence
region based on the directed Hausdorff distance. We would fail to reject
the null hypothesis that B is a subset of the population
identification region for the coefficient β_j if and only if
B \subseteq DU(β_j). The other option is "CC", the
confidence collection based on the undirected Hausdorff distance. We would
fail to reject the null hypothesis that [b_L, b_U] equals the
population identification region for β_j if and only if b_L
\in CC_L(β_j) and b_U \in CC_U(β_j).
The "DU" confidence region corresponds to
interval_hypothesis with type = "subset". The
"CC" confidence region corresponds to
interval_hypothesis with type = "equal".
Implements largely the same functionality as CI1D in Beresteanu et
al.'s (2010) Stata program.
A matrix containing the range of the confidence region for each term
requested. When type== "CC", there are two entries per term
(range of the confidence region for the lower and upper bound).
Brenton Kenkel
Arie Beresteanu and Francesca Molinari. 2008. "Asymptotic Properties for a Class of Partially Identified Models." Econometrica 76 (4): 763–814.
Arie Beresteanu, Francesca Molinari and Darcy Steeg Morris. 2010. "Asymptotics for Partially Identified Models in Stata." https://molinari.economics.cornell.edu/programs.html
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | ## Simulate data
set.seed(18)
x1 <- rnorm(50)
x2 <- rnorm(50)
y <- 1 - x1 + x2 + rnorm(50)
yl <- floor(y)
yu <- ceiling(y)
## Fit model
fit <- coefbounds(yl + yu ~ x1 + x2, boot = 100)
## Calculate DU confidence region for coefficient on x1
confint(fit, parm = "x1")
## Calculate CC confidence collections for all coefficients
confint(fit, type = "CC")
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