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st: Panel Data with Truncation and Gaps
Dear Stata and statistics experts,
I am looking for a strategy to handle a large amount of panel data
that features both truncation and gaps. In particular I would like to
know how I might go about fitting a model to the data I have on hand.
Important features of the data are as follows:
1. It is population data generated from agent-based evolutionary
simulations . Each trial population has a series of observations
associated with it over the length of time that it was being run.
2. To conserve memory and processing time two data collection
shortcuts were used.
2a. Summary statistics from the population were collected on the
initial creation of the population, after running it for one
generation, and again after the second generation. Following this the
same statistics are collected every five generation until generation
100 at which point the simulation of the population ends. If the
population drops below two members then no more information is
collected either (There is no single-agent reproduction).
2b. If the population grew over 15000 members then summary statistics
were collected in the generation in which this occurred and then the
population was dropped.
3. There are a collection of variables that need to be taken into
3a. Some of these are fixed throughout the trial (These include things
like the initial population size, the cost to live from generation to
generation, and the cost to spawn with another agent).
3b. Others change throughout the course of each simulation and are
randomly distributed at the beginning (These are the behaviours that
the agents exhibit under certain conditions. Over time as
opportunities to express these behaviours present themselves agents
with more good/useful beahviours get to spawn more, increasing the
likelihood that these useful behaviours will become more prevalent in
In particular I have two worries. First, that as successful
populations are truncated out, those that remain will bring down the
mean. Second, that a combination of successful population truncating
out and unsuccessful populations having few members with highly
similar behaviour sets will skew any investigation into which
behaviours are successful.
Any suggestions regarding possible models or methods for handling this
dataset or directions to possibly useful resources would be appreciated.
Department of Philosophy
University of Alberta, Canada
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