Confidence intervals for GMM or GEL
It produces confidence intervals for the coefficients from gel or gmm estimation.
## S3 method for class 'gel'
confint(object, parm, level = 0.95, lambda = FALSE,
type = c("Wald", "invLR", "invLM", "invJ"),
fact = 3, corr = NULL, ...)
## S3 method for class 'gmm'
confint(object, parm, level = 0.95, ...)
## S3 method for class 'ategel'
confint(object, parm, level = 0.95, lambda = FALSE,
type = c("Wald", "invLR", "invLM", "invJ"), fact = 3,
corr = NULL, robToMiss=TRUE, ...)
## S3 method for class 'confint'
print(x, digits = 5, ...)object |
An object of class |
parm |
A specification of which parameters are to be given confidence intervals, either a vector of numbers or a vector of names. If missing, all parameters are considered. |
level |
The confidence level |
lambda |
If set to TRUE, the confidence intervals for the Lagrange multipliers are produced. |
type |
'Wald' is the usual symetric confidence interval. The thee others are based on the inversion of the LR, LM, and J tests. |
fact |
This parameter control the span of search for the inversion of the test. By default we search within plus or minus 3 times the standard error of the coefficient estimate. |
corr |
This numeric scalar is meant to apply a correction to the critical value, such as a Bartlett correction. This value depends on the model (See Owen; 2001) |
x |
An object of class |
digits |
The number of digits to be printed |
robToMiss |
If |
... |
Other arguments when |
It returns a matrix with the first column being the lower bound and the second the upper bound.
Hansen, L.P. (1982), Large Sample Properties of Generalized Method of Moments Estimators. Econometrica, 50, 1029-1054, Hansen, L.P. and Heaton, J. and Yaron, A.(1996), Finit-Sample Properties of Some Alternative GMM Estimators. Journal of Business and Economic Statistics, 14 262-280. Owen, A.B. (2001), Empirical Likelihood. Monographs on Statistics and Applied Probability 92, Chapman and Hall/CRC
################# n = 500 phi<-c(.2,.7) thet <- 0 sd <- .2 x <- matrix(arima.sim(n = n, list(order = c(2,0,1), ar = phi, ma = thet, sd = sd)), ncol = 1) y <- x[7:n] ym1 <- x[6:(n-1)] ym2 <- x[5:(n-2)] H <- cbind(x[4:(n-3)], x[3:(n-4)], x[2:(n-5)], x[1:(n-6)]) g <- y ~ ym1 + ym2 x <- H t0 <- c(0,.5,.5) resGel <- gel(g, x, t0) confint(resGel) confint(resGel, level = 0.90) confint(resGel, lambda = TRUE) ######################## resGmm <- gmm(g, x) confint(resGmm) confint(resGmm, level = 0.90) ## Confidence interval with inversion of the LR, LM or J test. ############################################################## set.seed(112233) x <- rt(40, 3) y <- x+rt(40,3) # Simple interval on the mean res <- gel(x~1, ~1, method="Brent", lower=-4, upper=4) confint(res, type = "invLR") confint(res) # Using a Bartlett correction k <- mean((x-mean(x))^4)/sd(x)^4 s <- mean((x-mean(x))^3)/sd(x)^3 a <- k/2-s^2/3 corr <- 1+a/40 confint(res, type = "invLR", corr=corr) # Interval on the slope res <- gel(y~x, ~x) confint(res, "x", type="invLR") confint(res, "x")
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