[Gmm-commits] r12 - pkg/gmm/man

[noreply at r-forge.r-project.org]

Author: chaussep
Date: 2009-12-18 02:23:31 +0100 (Fri, 18 Dec 2009)
New Revision: 12

Removed:
   pkg/gmm/man/smooth_g.Rd
Log:
Removed the no longer necessary smooth_g.Rd


Deleted: pkg/gmm/man/smooth_g.Rd
===================================================================
--- pkg/gmm/man/smooth_g.Rd	2009-12-17 05:48:10 UTC (rev 11)
+++ pkg/gmm/man/smooth_g.Rd	2009-12-18 01:23:31 UTC (rev 12)
@@ -1,80 +0,0 @@
-\name{smooth_g}
-\alias{smooth_g}
-\title{Kernel smoothing of a matrix of time series}
-\description{
- It applies the required kernel smoothing to the moment function in order for the GEL estimator to be valid. It is used by the \code{gel} function.}
-\usage{
-smooth_g(x, bw = bwAndrews2, prewhite = 1, ar.method = "ols",weights=weightsAndrews2,
-	kernel=c("Bartlett","Parzen","Truncated","Tukey-Hanning"), 
-	approx = c("AR(1)","ARMA(1,1)"),tol = 1e-7) 
-}
-\arguments{
- \item{x}{a \eqn{n\times q} matrix of time series, where n is the sample size.}
- \item{bw}{The method to compute the bandwidth parameter. By default, it uses the bandwidth proposed by Andrews(1991). As an alternative, we can choose bw=bwNeweyWest2 (without "") which is proposed by Newey-West(1996).}
- \item{prewhite}{logical or integer. Should the estimating functions
-    be prewhitened? If \code{TRUE} or greater than 0 a VAR model of
-    order \code{as.integer(prewhite)} is fitted via \code{ar} with
-    method \code{"ols"} and \code{demean = FALSE}.}
-\item{ar.method}{character. The \code{method} argument passed to
-   \code{\link{ar}} for prewhitening.}
-\item{weights}{The smoothing weights can be computed by \code{\link{weightsAndrews2}} of it can be provided manually. If provided, it has to be a \eqn{r\times 1}vector (see details). }
-\item{approx}{a character specifying the approximation method if the
-    bandwidth has to be chosen by \code{bwAndrews2}.}
-\item{tol}{numeric. Weights that exceed \code{tol} are used for computing
-   the covariance matrix, all other weights are treated as 0.}
-\item{kernel}{The choice of kernel}
-}
-
-
-\details{
-\code{HAC} is simply a modified version of \code{meatHAC} from the package sandwich. The modifications have been made so that the argument x can be a matrix instead of an object of class lm or glm. The details on how is works can be found on the sandwich manual.
-
-The sample moment conditions \eqn{\sum_{t=1}^n g(\theta,x_t)} is replaced by:
-\eqn{\sum_{t=1}^n g^k(\theta,x_t)}, where \eqn{g^k(\theta,x_t)=\sum_{i=-r}^r k(i) g(\theta,x_{t+i})},
-where \eqn{r} is a truncated parameter that depends on the bandwidth and \eqn{k(i)} are normalized weights so that they sum to 1.
-
-If the vector of weights is provided, it gives only one side weights. For exemple, if you provide the vector (1,.5,.25), \eqn{k(i)} will become \eqn{(.25,.5,1,.5,.25)/(.25+.5+1+.5+.25) =  (.1,.2,.4,.2,.1)}
-}
-
-\value{
-smoothx: A \eqn{q \times q} matrix containing an estimator of the asymptotic variance of \eqn{\sqrt{n} \bar{x}}, where \eqn{\bar{x}} is \eqn{q\times 1}vector with typical element \eqn{\bar{x}_i = \frac{1}{n}\sum_{j=1}^nx_{ji}}. This function is called by \code{\link{gel}} but can also be used by itself.
-
-\code{kern_weights}: Vector of weights used for the smoothing.
-}
-
-\references{
-Anatolyev, S. (2005), GMM, GEL, Serial Correlation, and Asymptotic Bias. \emph{Econometrica}, \bold{73}, 983-1002.
-
-Andrews DWK (1991),
-Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation.
-\emph{Econometrica}, \bold{59},
-817--858.
-
-Kitamura, Yuichi (1997), Empirical Likelihood Methods With Weakly Dependent Processes.
-\emph{The Annals of Statistics}, \bold{25}, 2084-2102.
-
-Zeileis A (2006), Object-oriented Computation of Sandwich Estimators.
-\emph{Journal of Statistical Software}, \bold{16}(9), 1--16.
-URL \url{http://www.jstatsoft.org/v16/i09/}.
-}
-
-\examples{
-g <- function(tet,x)
-	{
-	n <- nrow(x)
-	u <- (x[7:n] - tet[1] - tet[2]*x[6:(n-1)] - tet[3]*x[5:(n-2)])
-	f <- cbind(u,u*x[4:(n-3)],u*x[3:(n-4)],u*x[2:(n-5)],u*x[1:(n-6)])
-	return(f)
-	}
-n = 500
-phi<-c(.2,.7)
-thet <- 0.2
-sd <- .2
-x <- matrix(arima.sim(n=n,list(order=c(2,0,1),ar=phi,ma=thet,sd=sd)),ncol=1)
-gt <- g(c(0,phi),x) 
-sgt <- smooth_g(gt)$smoothx
-plot(gt[,1])
-lines(sgt[,1])
-}
-
-