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st: Panel Data with Truncation and Gaps


From   John Simpson <john.simpson@ualberta.ca>
To   statalist@hsphsun2.harvard.edu
Subject   st: Panel Data with Truncation and Gaps
Date   Sat, 4 Jul 2009 23:27:56 -0600

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 account.

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 the population).


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.

John Simpson
Department of Philosophy
University of Alberta, Canada
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