*
* Graph examples from page 19
*
calendar(m) 1926:1
*
* The IBM data are simple returns.
*
open data ch01_ibmvwewsp_m(26-03).txt
data(format=free,org=columns) 1926:1 2003:12 date ibmsmp
set ibmlog = log(ibmsmp+1)
*
* Scale returns up to percentages
*
set ibmsmp = 100*ibmsmp
set ibmlog = 100*ibmlog
*
* This uses SPGRAPH to create a graph with two horizontal panes. Note that the
* appearance of the empirical densities depends upon the choice of bandwidth and
* window type, and also upon the coarseness of the grid of "x" values at which the
* density is computed. The default bandwidth in the DENSITY instruction appears to
* be a bit narrower than the one used in Figure 1.4, and the grid used here is a
* bit finer, leading to more rounded features. (The RATS graph of the density
* will also be a set of line segments, but they will be shorter, with more
* frequent slope changes, and thus will look more like a curve).
*
spgraph(hfields=2,footer="Figure 1.4 Comparison of Empirical and Normal Densities for IBM Stock Returns")
stats ibmsmp
density ibmsmp / xgrid fsmp
set nsmp = 1.0/sqrt(%variance)*%density((xgrid-%mean)/sqrt(%variance))
scatter(style=lines,hlabel="simple-rtn",vlabel="density") 2
# xgrid fsmp
# xgrid nsmp
*
density ibmlog / xgrid flog
set nlog = 1.0/sqrt(%variance)*%density((xgrid-%mean)/sqrt(%variance))
scatter(style=lines,hlabel="log-rtn",vlabel="density") 2
# xgrid flog
# xgrid nlog
spgraph(done)