Abstract #300299

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JSM 2003 Abstract #300299
Activity Number: 91
Type: Contributed
Date/Time: Monday, August 4, 2003 : 8:30 AM to 10:20 AM
Sponsor: Social Statistics Section
Abstract - #300299
Title: Recovering Transitions from Repeated Cross-Sections with Entropy and Likelihood
Author(s): Rob Eisinga*+ and Ben Pelzer and George G. Judge
Companies: University of Nijmegen and University of Nijmegen and University of California
Address: Dept. Social Science Research Methods, Nijmegen, , 6500 HE, Netherlands
Keywords: repeated cross-section ; maximum entropy ; maximum likelihood ; Markov model ; survey
Abstract:

This paper investigates a dynamic Markov model for the estimation of state-to-state transitions from a sequence of independent cross-sectional samples. It proposes a novel method for recovering the unknown transition probabilities in repeated cross-sections based on the maximum entropy principle. This new approach is compared with ordinary maximum likelihood estimation. The model is illustrated by an application to pupils' interest in learning physics using a three-wave panel study. These panel data encompass more information than we need to estimate the model, but this additional information allows us to assess the accuracy and precision of the transition estimates. To mimic genuine repeated cross-sectional data, samples of independent observations randomly drawn from the panel are also analyzed.

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