1\name{numEff}
2\alias{numEff}
3\concept{numerical efficiency}
4
5\title{Compute Numerical Standard Error and Relative Numerical Efficiency}
6
7\description{
8\code{numEff} computes the numerical standard error for the mean of a vector of draws as well as the relative numerical efficiency (ratio of variance of mean of this time series process relative to iid sequence).
9}
10
11\usage{numEff(x, m = as.integer(min(length(x),(100/sqrt(5000))*sqrt(length(x)))))}
12
13\arguments{
14  \item{x}{ \eqn{R x 1} vector of draws }
15  \item{m}{ number of lags for autocorrelations }
16}
17
18\details{
19default for number of lags is chosen so that if \eqn{R=5000}, \eqn{m=100} and increases as the \eqn{sqrt(R)}.
20}
21
22\value{
23  A list containing:
24  \item{stderr }{standard error of the mean of \eqn{x}}
25  \item{f }{ variance ratio (relative numerical efficiency) }
26}
27
28\section{Warning}{
29This routine is a utility routine that does \strong{not} check the input arguments for proper dimensions and type.
30}
31
32\author{Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}.}
33
34\references{For further discussion, see Chapter 3, \emph{Bayesian Statistics and Marketing} by Rossi, Allenby, and McCulloch. \cr \url{http://www.perossi.org/home/bsm-1}}
35
36\examples{
37numEff(rnorm(1000), m=20)
38numEff(rnorm(1000))
39}
40
41\keyword{ts}
42\keyword{utilities}
43