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Assessing cohort aggregation to minimise bias in pseudo-panels

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  • Rumman Khan

Abstract

Pseudo-panels allow estimation of panel models when only repeated cross-sections are available. This involves grouping individuals into cohorts and using the cohort means as if they are observations in a genuine panel. Their practical use is constrained by a lack of consensus on how the pseudo-panels should be formed, particularly to address potential sampling error bias. We show that grouping can also create substantial aggregation bias, calling into question how well pseudo-panels can mimic panel estimates. We create two metrics for assessing the grouping process, one for each potential source of bias. If both metrics are above certain recommended values, the biases from aggregation and sampling error are minimised, meaning results can be interpreted as if they were from genuine panels.

Suggested Citation

  • Rumman Khan, 2018. "Assessing cohort aggregation to minimise bias in pseudo-panels," Discussion Papers 2018-01, University of Nottingham, CREDIT.
  • Handle: RePEc:not:notcre:18/01
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    Keywords

    pseudo-panel; estimation bias; sampling error; aggregation bias; repeated cross-section; household surveys;

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