<h2>File List</h2><blockquote>
<p> Matlab code: </p>
<p> <a href="gensimests.m">gensimests.m</a> <br>
<a href="marshmat.m">marshmat.m</a> <br>
<a href="paramperf.m">paramperf.m</a> <br>
<a href="SimpleRun.m">SimpleRun.m</a> </p>
<p> All files are in ASCII text.
</p>
</blockquote><h2>Description</h2>The following are matlab files (in ascii format)
to run 1000 simulations of a stochastic matrix model and then graph diagnostic
plots of the parameter and risk metric estimates using ML, running sum, Heyde-Cohen,
Kalman and slope parameterization methods. To run, copy the files to a directory
and run the "SimpleRun.m" script. This calls functions to run simulations and
make plots described in the other files.
<p> The file SimpleRun.m calls marshmat.m to specify the stochastic model. It then calls
gensimests.m to make 1000 simulated time series,
estimate the diffusion approximation parameters using the ML, running sum, Heyde-Cohen,
Kalman and slope methods, and saves the results to a file. Finally, SimpleRun.m
calls paramperf.m which makes diagnostic plots of
the different parameterization methods. </p>
<p> The file marshmat.m specifies the matrix model, the level of stochasticity
for each matrix element, and what segment of the population is censused.
</p>
<p> The file gensimests.m runs the model specified in marshmat.m to create simulated time series. To each time
series, it adds either none or one of the three levels of sampling error. From
this time series, it then estimates diffusion approximation parameters via ML,
running sum, Heyde-Cohen, Kalman or slope methods. It saves the results in a
data file. </p>
<p> The file paramperf.m takes the data file created by gensimests.m and makes diagnostic plots of the percentage
error in estimation of m, s<sup>2</sup>, l,
and the probability of 90% decline. </p>
<p>
</p
