kernapply-methods {gmm4} | R Documentation |
"gelModels"
classesIt either generates the optimal bandwidth and kernel weights or the smoothed moments of GEL models.
## S4 method for signature 'gelModels' kernapply(x, theta=NULL, smooth=TRUE, ...) ## S4 method for signature 'gmmModels' kernapply(x, theta=NULL, ...)
x |
An object of class |
theta |
An optional vector of coefficients. For
|
smooth |
By default, it returns the smoothed moment matrix. If
|
... |
Other arguments to pass. Currently not used |
A list which contains:
k |
2\times 1 vector of scaling factors used for GEL asymptotics. See Anatolyev (2005). |
w |
The kernel weights as an object of class "tskernel". See
|
bw |
A numeric bandwidth. |
kernel |
A character specifying th type of kernel used for smoothing |
smoothx |
Only when |
Anatolyev, S. (2005), GMM, GEL, Serial Correlation, and Asymptotic Bias. Econometrica, 73, 983-1002.
Kitamura, Yuichi (1997), Empirical Likelihood Methods With Weakly Dependent Processes. The Annals of Statistics, 25, 2084-2102.
Smith, R.J. (2011), GEL Criteria for Moment Condition Models. Econometric Theory, 27(6), 1192–1235.
data(simData) theta <- c(beta0=1,beta1=2) ## A linearGmm model1 <- gmmModel(y~x1, ~z1+z2, data=simData,vcov="HAC",vcovOptions=list(kernel="Bartlett")) ### get the bandwidth ### Notice that the kernel name is the not the same ### That's because a Truncated kernel for smoothing ### lead to a Bartlett kernel for the HAC of the moments ### See Smith (2011) kernapply(model1, smooth=FALSE) ### The GEL model contains the info when it is created model2 <- gmmToGel(model1, "EL") model2@wSpec kernapply(model2, theta)$smoothx[1:5,]