kernapply-methods {momentfit} | R Documentation |
"momentModel"
classesIt either generates the optimal bandwidth and kernel weights or the smoothed moments of moment based models.
## S4 method for signature 'momentModel' kernapply(x, theta=NULL, smooth=TRUE, ...)
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 |
It return an object of class "sSpec"
.
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 linearModel model1 <- momentModel(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) ### Adding the kernel option to the model model2 <- momentModel(y~x1, ~z1+z2, data=simData,vcov="HAC",vcovOptions=list(kernel="Bartlett"), smooth=TRUE) kernapply(model2, theta)$smoothx[1:5,]