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Likelihood-Based Estimators for Endogenous or Truncated Samples in Standard Stratified Sampling

In: Missing Data Methods: Cross-sectional Methods and Applications

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  • Myoung-jae Lee
  • Sanghyeok Lee

Abstract

Standard stratified sampling (SSS) is a popular non-random sampling scheme. Maximum likelihood estimator (MLE) is inconsistent if some sampled strata depend on the response variable Y (‘endogenous samples’) or if some Y-dependent strata are not sampled at all (‘truncated sample’ – a missing data problem). Various versions of MLE have appeared in the literature, and this paper reviews practical likelihood-based estimators for endogenous or truncated samples in SSS. Also a new estimator ‘Estimated-EX MLE’ is introduced using an extra random sample on X (not on Y) to estimate the distribution EX of X. As information on Y may be hard to get, this estimator's data demand is weaker than an extra random sample on Y in some other estimators. The estimator can greatly improve the efficiency of ‘Fixed-X MLE’ which conditions on X, even if the extra sample size is small. In fact, Estimated-EX MLE does not estimate the full FX as it needs only a sample average using the extra sample. Estimated-EX MLE can be almost as efficient as the ‘Known-FX MLE’. A small-scale simulation study is provided to illustrate these points.

Suggested Citation

  • Myoung-jae Lee & Sanghyeok Lee, 2011. "Likelihood-Based Estimators for Endogenous or Truncated Samples in Standard Stratified Sampling," Advances in Econometrics, in: Missing Data Methods: Cross-sectional Methods and Applications, pages 63-91, Emerald Group Publishing Limited.
    • Handle: RePEc:eme:aecozz:s0731-9053(2011)000027a006
    DOI: 10.1108/S0731-9053(2011)000027A006
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