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SELECTIVITY BIAS CORRECTION METHODS IN POLYCHOTOMOUS SAMPLE SELECTION MODELS
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AI
DOI：10.1016/0304-4076(94)90039-6.png)
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Abstract
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Polychotomous sample selection models include multinomial choice models and models with multiple rules for sample inclusion. The author analyzes two standard parametric approaches to selectivity bias correction in such models - the Lee and Generalized Heckman (GH) methods. The paper's main point is that Lee's approach, unlike GH, requires strong implicit restrictions on covariances between outcomes and selection indices. A Monte Carlo study demonstrates (1) that the Lee estimator exhibits significant bias when the data-generating process does not conform to its implicit covariance assumptions, and (2) that the GH estimator may have high variance due to multicollinearity between regressors.
Keywords:
SELECTIVITY BIAS
SELF-SELECTION
NONRANDOM SAMPLES
POLYCHOTOMOUS CHOICE
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4
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5.3K
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