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Ecological panel inference in repeated cross sections

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  • Pelzer, B.
  • Eisinga, R.
  • Franses, Ph.H.B.F.

Abstract

This paper presents a Markov chain model for the estimation of individual-level binary transitions from a time series of independent repeated cross-sectional (RCS) samples. Although RCS samples lack direct information on individual turnover, it is demonstrated here that it is possible with these data to draw meaningful conclusions on individual state-to-state transitions. We discuss estimation and inference using maximum likelihood, parametric bootstrap and Markov chain Monte Carlo approaches. The model is illustrated by an application to the rise in ownership of computers in Dutch households since 1986, using a 13-wave annual panel data set. These data encompass more information than we need to estimate the model, but this additional information allows us to assess the validity of the parameter estimates. We examine the determinants of the transitions from 'have-not' to 'have' (and back again) using well-known socio-economic and demographic covariates of the digital divide. Parametric bootstrap and Bayesian simulation are used to evaluate the accuracy and the precision of the ML estimates and the results are also compared with those of a first-order dynamic panel model. To mimic genuine repeated cross-sectional data, we additionally analyse samples of independent observations randomly drawn from the panel. Software implementing the model is available.

Suggested Citation

  • Pelzer, B. & Eisinga, R. & Franses, Ph.H.B.F., 2002. "Ecological panel inference in repeated cross sections," Econometric Institute Research Papers EI 2002-22, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    • Handle: RePEc:ems:eureir:560
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    References listed on IDEAS

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    1. Pelzer, Ben & Eisinga, Rob & Franses, Philip Hans, 2002. "Inferring Transition Probabilities from Repeated Cross Sections," Political Analysis, Cambridge University Press, vol. 10(2), pages 113-133, April.
    2. Oecd, 2001. "Understanding the Digital Divide," OECD Digital Economy Papers 49, OECD Publishing.
    3. Jarque, Carlos M. & Bera, Anil K., 1980. "Efficient tests for normality, homoscedasticity and serial independence of regression residuals," Economics Letters, Elsevier, vol. 6(3), pages 255-259.
    4. Gary King & Ori Rosen & Martin A. Tanner, 1999. "Binomial-Beta Hierarchical Models for Ecological Inference," Sociological Methods & Research, , vol. 28(1), pages 61-90, August.
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