The Heckman correction is a statistical technique to correct bias from non-randomly selected samples or otherwise incidentally truncated dependent variables, a pervasive issue in quantitative social sciences when using observational data.[1] Conceptually, this is achieved by explicitly modelling the individual sampling probability of each observation (the so-called selection equation) together with the conditional expectation of the dependent variable (the so-called outcome equation). The resulting likelihood function is mathematically similar to the tobit model for censored dependent variables, a connection first drawn by James Heckman in 1974.[2] Heckman also developed a two-step control function approach to estimate this model,[3] which avoids the computational burden of having to estimate both equations jointly, albeit at the cost of inefficiency.[4] Heckman received the Nobel Memorial Prize in Economic Sciences in 2000 for his work in this field.[5]
^Winship, Christopher; Mare, Robert D. (1992). "Models for Sample Selection Bias". Annual Review of Sociology. 18: 327–350. doi:10.1146/annurev.so.18.080192.001551.
^Heckman, James (1974). "Shadow Prices, Market Wages, and Labor Supply". Econometrica. 42 (4): 679–694. doi:10.2307/1913937. JSTOR 1913937.
^Heckman, James (1976). "The Common Structure of Statistical Models of Truncation, Sample Selection and Limited Dependent Variables and a Simple Estimator for Such Models". Annals of Economic and Social Measurement. 5 (4): 475–492.
^Nawata, Kazumitsu (1994). "Estimation of Sample Selection Bias Models by the Maximum Likelihood Estimator and Heckman's Two-Step Estimator". Economics Letters. 45 (1): 33–40. doi:10.1016/0165-1765(94)90053-1.
^Uchitelle, Louis (October 12, 2000). "2 Americans Win the Nobel For Economics". New York Times.
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