\name{spectrum0} \alias{spectrum0} \title{Estimate spectral density at zero} \description{ The spectral density at frequency zero is estimated by fitting a glm to the low-frequency end of the periodogram. \code{spectrum0(x)/length(x)} estimates the variance of \code{mean(x)}. } \usage{ spectrum0(x, max.freq = 0.5, order = 1, max.length = 200) } \arguments{ \item{x}{A time series.} \item{max.freq}{The glm is fitted on the frequency range (0, max.freq]} \item{order}{Order of the polynomial to fit to the periodogram.} \item{max.length}{The data \code{x} is aggregated if necessary by taking batch means so that the length of the series is less than \code{max.length}. If this is set to \code{NULL} no aggregation occurs.} } \details{ The raw periodogram is calculated for the series \code{x} and a generalized linear model with family \code{Gamma} and log link is fitted to the periodogram. The linear predictor is a polynomial in terms of the frequency. The degree of the polynomial is determined by the parameter \code{order}. } \value{ A list with the following values \item{spec}{The predicted value of the spectral density at frequency zero.} } \references{ Heidelberger, P and Welch, P.D. A spectral method for confidence interval generation and run length control in simulations. Communications of the ACM, Vol 24, pp233-245, 1981. } \section{Theory}{ Heidelberger and Welch (1991) observed that the usual non-parametric estimator of the spectral density, obtained by smoothing the periodogram, is not appropriate for frequency zero. They proposed an alternative parametric method which consisted of fitting a linear model to the log periodogram of the batched time series. Some technical problems with model fitting in their original proposal can be overcome by using a generalized linear model. Batching of the data, originally proposed in order to save space, has the side effect of flattening the spectral density and making a polynomial fit more reasonable. Fitting a polynomial of degree zero is equivalent to using the `batched means' method. } \note{ The definition of the spectral density used here differs from that used by \code{spec.pgram}. We consider the frequency range to be between 0 and 0.5, not between 0 and \code{frequency(x)/2}. The model fitting may fail on chains with very high autocorrelation. } \seealso{ \code{\link{spectrum}}, \code{\link{spectrum0.ar}}, \code{\link{glm}}. } \keyword{ts}