Details
| Original language | English |
|---|---|
| Pages (from-to) | 53-68 |
| Number of pages | 16 |
| Journal | Journal of economic surveys |
| Volume | 14 |
| Issue number | 1 |
| Publication status | Published - Feb 2000 |
| Externally published | Yes |
Abstract
This paper gives a short overview of Monte Carlo studies on the usefulness of Heckman's (1976, 1979) two-step estimator for estimating selection models. Such models occur frequently in empirical work, especially in microeconometrics when estimating wage equations or consumer expenditures. It is shown that exploratory work to check for collinearity problems is strongly recommended before deciding on which estimator to apply. In the absence of collinearity problems, the full-information maximum likelihood estimator is preferable to the limited-information two-step method of Heckman, although the latter also gives reasonable results. If, however, collinearity problems prevail, subsample OLS (or the Two-Part Model) is the most robust amongst the simple-to-calculate estimators.
Keywords
- Estimator performance, OLS, Sample selection model, Two-part model
ASJC Scopus subject areas
- Economics, Econometrics and Finance(all)
- Economics and Econometrics
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In: Journal of economic surveys, Vol. 14, No. 1, 02.2000, p. 53-68.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - The Heckman correction for sample selection and its critique
AU - Puhani, Patrick A.
PY - 2000/2
Y1 - 2000/2
N2 - This paper gives a short overview of Monte Carlo studies on the usefulness of Heckman's (1976, 1979) two-step estimator for estimating selection models. Such models occur frequently in empirical work, especially in microeconometrics when estimating wage equations or consumer expenditures. It is shown that exploratory work to check for collinearity problems is strongly recommended before deciding on which estimator to apply. In the absence of collinearity problems, the full-information maximum likelihood estimator is preferable to the limited-information two-step method of Heckman, although the latter also gives reasonable results. If, however, collinearity problems prevail, subsample OLS (or the Two-Part Model) is the most robust amongst the simple-to-calculate estimators.
AB - This paper gives a short overview of Monte Carlo studies on the usefulness of Heckman's (1976, 1979) two-step estimator for estimating selection models. Such models occur frequently in empirical work, especially in microeconometrics when estimating wage equations or consumer expenditures. It is shown that exploratory work to check for collinearity problems is strongly recommended before deciding on which estimator to apply. In the absence of collinearity problems, the full-information maximum likelihood estimator is preferable to the limited-information two-step method of Heckman, although the latter also gives reasonable results. If, however, collinearity problems prevail, subsample OLS (or the Two-Part Model) is the most robust amongst the simple-to-calculate estimators.
KW - Estimator performance
KW - OLS
KW - Sample selection model
KW - Two-part model
UR - http://www.scopus.com/inward/record.url?scp=0034009792&partnerID=8YFLogxK
U2 - 10.1111/1467-6419.00104
DO - 10.1111/1467-6419.00104
M3 - Article
AN - SCOPUS:0034009792
VL - 14
SP - 53
EP - 68
JO - Journal of economic surveys
JF - Journal of economic surveys
SN - 0950-0804
IS - 1
ER -