timesboot (version 1.0)

boot_autocov: A function that computes the bootstrapped autocovariances for a time series object. The computations are done via phase scrambling bootstrap

Description

The function resamples the time series object and returns the average , upper, and lower bounds for the autocovariances for each lag.

Usage

boot_autocov(series, replic = 5000, plot = TRUE, alpha = 0.05)

Arguments

series

A time series object

replic

The amount of boostrap replicates

plot

TRUE,FALSE indicating whether the plot is desired

alpha

the alpha needed for the intervals

Value

average

The average ACF for each lag

lower

The ACF lower quantile for each lag

upper

The ACF upper quantile for each lag

Examples

boot_autocov(AirPassengers,replic=1000,alpha=0.05)


function (series, replic = 5000, plot = TRUE, alpha = 0.05) 
{
    if (is.ts(series) == TRUE) {
        library(boot)
        kas = tsboot(series, statistic, R = replic, sim = "scramble")
        quantiles = matrix(0, length(kas$t[1, ]), 3)
        for (i in 2:length(kas$t[1, ])) {
            cp = kas$t[, i]
            quantiles[i, 1] = quantile(cp, alpha)
            quantiles[i, 2] = quantile(cp, 1 - alpha/2)
            quantiles[i, 3] = mean(cp)
        }
        quantiles = quantiles[-1, ]
        if (plot == TRUE) {
            par(mfrow = c(1, 2))
            x = seq(1, length(quantiles[, 1]), 1)/frequency(series)
            plot(x, quantiles[, 1], type = "l", col = "blue", 
                main = "Bootstraped Correlogram", ylab = "value", 
                lwd = 1, xlab = "lag")
            polygon(c(x, rev(x)), c(quantiles[, 1], rev(quantiles[, 
                2])), col = "skyblue")
            lines(x, quantiles[, 3], type = "o", col = "black", 
                pch = 20)
            abline(a = 0, b = 0)
            plot(acf(series, plot = FALSE), main = "Asymptotic Correlogram", 
                ylim = c(-1, 1))
        }
        lista = list(average = quantiles[, 1], upper = quantiles[, 
            2], lower = quantiles[, 3])
        return(lista)
    }
    else {
        return("Object is not a time-series")
    }
  }