This function just fulfills a very naive idea about moving window regression using rectangles to denote the ``windows'' and move them, and the corresponding AR(1) coefficients as long as rough confidence intervals are computed for data points inside the ``windows'' during the process of moving.
mwar.ani(x, k = 15, conf = 2, mat = matrix(1:2, 2), widths = rep(1, ncol(mat)),
heights = rep(1, nrow(mat)), lty.rect = 2, ...)univariate time-series (a single numerical vector); default to be
sin(seq(0, 2 * pi, length = 50)) + rnorm(50, sd = 0.2)
an integer of the window width
a positive number: the confidence intervals are computed as
c(ar1 - conf*s.e., ar1 + conf*s.e.)
arguments passed to layout to divide
the device into 2 parts
the line type of the rectangles respresenting the moving ``windows''
other arguments passed to points in the bottom
plot (the centers of the arrows)
A list containing
the AR(1) coefficients
lower bound of the confidence interval
upper bound of the confidence interval
The AR(1) coefficients are computed by arima.
Robert A. Meyer, Jr. Estimating coefficients that change over time. International Economic Review, 13(3):705-710, 1972.